However, our understanding of their physiological functions and molecular regulation has been limited by the lack of proper mutant mouse models allowing their conditional genetic targeting. Because the Xcr1 and A530099j19rik (Karma/Gpr141b) genes belong to the core transcriptomic fingerprint of mouse cDC1, we used them to engineer two novel Cre-driver lines, the Xcr1Cre and KarmaCre mice, by knocking in an IRES-Cre expression cassette into their 3′-UTR. We used genetic tracing to characterize the specificity and efficiency of these new models in several lymphoid and non-lymphoid tissues, and compared them to the Clec9aCre mouse model, which targets the immediate precursors of cDCs. Amongst the three Cre-driver mouse models examined, the Xcr1Cre model was the most efficient and specific for the fate mapping of all cDC1, regardless of the tissues examined. The KarmaCre model was rather specific for cDC1 when compared with the Clec9aCre mouse, but less efficient than the Xcr1Cre model. Unexpectedly, the Xcr1Cre model targeted a small fraction of CD4+ T cells, and the KarmaCre model a significant proportion of mast cells in the skin. Importantly, the targeting specificity of these two mouse models was not changed upon inflammation. A high frequency of germline recombination was observed solely in the Xcr1Cre mouse model when both the Cre and the floxed alleles were brought by the same gamete irrespective of its gender. Xcr1, Karma, and Clec9a being differentially expressed within the cDC1 population, the three CRE-driver lines examined showed distinct recombination patterns in cDC1 phenotypic subsets. This advances our understanding of cDC1 subset heterogeneity and the differentiation trajectory of these cells. Therefore, to the best of our knowledge, upon informed use, the Xcr1Cre and KarmaCre mouse models represent the best tools currently reported to specifically and faithfully target cDC1 in vivo, both at steady state and upon inflammation. Future use of these mutant mouse models will undoubtedly boost our understanding of the biology of cDC1.Type 1 conventional DCs (cDC1) excel in the cross-priming of CD8 The functions of cDCs and their molecular regulation have been studied in vivo by using a wealth of mouse models that enable their depletion or genetic manipulation, namely Cd11c (Itgax)hDTR (Cd11c (Itgax)Cre  constitute a heterogeneous population of antigen presenting cells (APCs) which are instrumental for the orchestration of innate and adaptive immune responses. In mice and in humans, three distinct types of DCs differing in their phenotype, localization and functions populate all lymphoid and most non-lymphoid tissues at steady state. Plasmacytoid DCs (pDCs) are the major source of type I interferon (IFN) upon many viral infections. Conventional DCs (cDCs) consist of two populations, coined as type 1 and type 2 cDCs and which excel in the cross-priming of CD8tgax)Cre  and morebtb46Cre . HoweverBatf3-KO mice) or conditional  genetic inactivation of transcription factors required for the differentiation of cDC1 allowed to study their specific functions in vivo  cells, CD8+ T cells and memory T cells upon activation in mice Ksho mouse model has been useful to decipher the role of cDC1 in intraepithelial T cell homeostasis in the intestine  has also been identified as selectively expressed in cDC1 by bulk transcriptomic analysis on immune cell subsets and organs  and the human diphtheria toxin receptor (hDTR), allowing specific tracking and conditional depletion of cDC1 in vivo. Results obtained with this reporter mouse validated the Karma locus as highly reliable to functionally target cDC1 in vivo  and KarmaCre (B6-Gpr141btm2Ciphe) mice were made according to a standard gene targeting approach in C57BL/6N-derived ES cells. They were constructed by inserting, through ET homologous recombination, a cassette containing the internal ribosome entry site (IRES) followed by a gene encoding the codon-improved version of Cre recombinase  C57BL/6J mice purchased from Charles River Laboratories. All experiments were performed with sex-matched littermate mice at 6–12 weeks of age. Clec9aCre (Clec9atm2.1(icre)Crs) (Karma-tdTomato-hDTR (Gp141btm1Ciphe) (Rosa26lox−stop−lox−tdRFP (Gt(ROSA)26Sortm1Hjf) mice in which expression of the tandem dimer Red Fluorescent Protein (tdRFP) is driven through the deletion of a “lox–stop–lox” sequence (Rosa26lox−stop−lox−DTA (Gt(ROSA)26Sortm1(DTA)Lky) mice in which expression of active domain of the diphtheria toxin (DTA) is driven through the deletion of a “lox–stop–lox” sequence Crs)  knock-inm1Ciphe)  were maisequence  were pursequence  were obt4Cl, 10 mM KHCO3, 0.127 M EDTA to lyse red blood cells. Liver and lungs were minced in an enzymatic cocktail (1 mg/ml of Collagenase D and 70 μg/ml of DNase I), incubated for 25 min at 37°C. Ice-cold EDTA (2 mM) was added for additional 5 min, then digested tissues were filtered through a 70 μm nylon sieve . Low-density cells were further enriched by centrifugation over a 1.069 g/ml density gradient , washed and resuspended in PBS, EDTA 2 mM, 2% BSA, and red blood cells were lysed as detailed above. Cutaneous LNs  were cut into small pieces and digested for 25 min at 37°C with a mixture of type II collagenase  and DNase I (Sigma-Aldrich) in plain RPMI 1640. The resulting cell suspension was treated with 5 mM EDTA and filtered through a 70 μm nylon sieve . For the skin, ears were split into a ventral and dorsal parts and incubated for 105 min at 37°C in RPMI containing 0.25 mg/ml Liberase TL (Roche Diagnostic Corp.) and 0.5 mg/ml DNase I (Sigma Aldrich). Digested tissue was homogenized using Medicons and Medimachine (Becton Dickinson) to obtain homogenous cell suspensions. For skin mast cells, we used a protocol recently described (Splenocytes were prepared by infusing spleens with an enzymatic cocktail made of Collagenase D (1 mg/ml) and DNase I  in plain RPMI 1640, and further incubation for 25 min at 37°C. Ice cold EDTA (2 mM) was added for additional 5 min. Cells were filtered through a 70-μm nylon sieve, and exposed to 0.155 M NHescribed  was then6 cells/ml in 24 well plates, with 10% FBS, RPMI 1640 medium containing murine FLT3-L  at 37°C in 5% CO2. Four days after, half of the culture medium was replaced by fresh FLT3-L. Cells were harvested at indicated times for flow cytometry analysis after staining for CD11c, SiglecH, CD24, SIRPα, XCR1, and CD11b  was used as starting material for each sample to synthesize biotinylated probes, using the NuGen protocol as described previously (https://software.broadinstitute.org/morpheus/). Hierarchical clusterings were performed using the One-Pearson correlation as a metric and the average linkage as a clustering method for samples and genes, except for Figure 5B where the complete linkage method was used for the genes. The microarray data have been deposited in the GEO database under the series accession number GSE121859.DCs were generated eviously . Affymeteviously . Heatmap5 PFU of salivary gland-extracted MCMV Smith strain (3rd in vivo passage). Forty-Eight hours later, spleen and liver were harvested and prepared for flow cytometry analysis as described above.Animals were infected intraperitoneally with 2 × 10Xcr1Cre/wt; Rosa26tdRFP/wt and KarmaCre/wt; Rosa26tdRFP/wt genotype were backcrossed to C57BL/6J mice. Their progeny was genotyped and bled to analyse tdRFP expression in circulating T and B cells as a sign of germline recombination.Four females and 2 males of each Xcr1 and a530099j19rik (Gpr141b/Karma) genes as specifically expressed by mouse cDC1 in different tissues throughout the body   of CD4+ T cells expressed tdRFP in the spleen, CLNs, lung and liver, which increased to a much higher fraction of CD4+ T cells in the skin (8.4 ± 6.4%). The CD4+ T cells harboring Xcr1-driven Cre recombination lacked detectable level of XCR1  also expressed the tdRFP  , and ana26tdRFP)  and non-26tdRFP) –E. To de26tdRFP) . As a coCre mice  bred to + cells   was lower than that of XCR1+CD8α+ cDC1 (69.0 ± 2.1%). This suggest that most of XCR1−CD8α+ cDC1 do not derive from XCR1+CD8α+ cDC1 contrary to our expectation that the majority of the former cells correspond to an advanced maturation state of the latter ones. Altogether, the combined use of our fate mapping mouse models suggests consecutive expression of the corresponding genes along the differentiation of the cDC1 lineage in the spleen, with Clec9a expressed from the common cDC progenitor stage, Xcr1 likely starting at the pre-cDC1 stage and Karma turned on only at a later stage similarly to CD8α.To try understanding the lower efficiency of the + cells) . CD24+SI1 Figure , 42. Themogenous , 42, andmogenous . Howeverpre-cDC1 , 44. Thee thymus , 32, 39.8α+ pDCs . ConsistDC stage , Clec9aCin vitro generation of three subsets of DCs, which are phenotypically and functionally equivalent to in vivo cDC1 (eq-cDC1), cDC2 (eq-cDC2) and pDCs (eq-pDCs)   genes  cells cultured with Fms-like tyrosine kinase 3 ligand (FLT3-L) . This moeq-pDCs) , 48–50. eq-pDCs) . Xcr1Cre) Figure . Interess Figure . Consistn Figure , it alsorma gene   infection, using the Rosa26tdRFP reporter as a read out. We adapted a gating strategy adapted from . This indicates that, contrary to what the results of the Rosa26tdRFP/wt backcross appears to suggest, off-target activity of the Cre recombinase in germline is not a gender effect. Additionally, the incidence of germline recombination depended on the loxP-flanked allele (data not shown). No occurrence of germline recombination was detected so far for the KarmaCre model  genes code for the chemokine receptor XCR1 and for the putative G protein-coupled receptor Gpr141b, respectively, and are among the core gene signature specifically identifying mouse cDC1 throughout the organism  show that mast cells from the skin, peritoneal cavity, trachea and esophagus express high level of the Karma gene   . We showand Ly75 . Of note . We shody state . This cody state , 58 and combined  might alXcr1Cre mouse model as a robust tool to inactivate genes selectively in cDC1 either in vivo or in vitro using BM-derived DC cultures. Future use of these mutant mouse models will undoubtedly boost the advancing of our understanding of the biology of cDC1.Our results advanced our understanding of the differentiation trajectory of cDC1, and validated the MD and KC: Conceptualization. RM, CW, BM, MD, and KC: Methodology, Validation, Formal Analysis. RM, CW, SG, MA, YA, CS, and AF: Investigation. T-PV, MD: bioinformatics analyses. BM: Mouse model construction supervision. RM, KC: Visualization. RM, KC, and MD: Writing. All authors: Editing. BM, MD, and KC: Project Supervision and Administration.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The development of science and technology of advanced materials using nanoscale units can be conducted by a novel concept involving combination of nanotechnology methodology with various research disciplines, especially supramolecular chemistry. The novel concept is called ‘nanoarchitectonics’ where self-assembly processes are crucial in many cases involving a wide range of component materials. This review of self-assembly processes re-examines recent progress in materials nanoarchitectonics. It is composed of three main sections: (1) the first short section describes typical examples of self-assembly research to outline the matters discussed in this review; (2) the second section summarizes self-assemblies at interfaces from general viewpoints; and (3) the final section is focused on self-assembly processes at interfaces. The examples presented demonstrate the strikingly wide range of possibilities and future potential of self-assembly processes and their important contribution to materials nanoarchitectonics. The research examples described in this review cover variously structured objects including molecular machines, molecular receptors, molecular pliers, molecular rotors, nanoparticles, nanosheets, nanotubes, nanowires, nanoflakes, nanocubes, nanodisks, nanoring, block copolymers, hyperbranched polymers, supramolecular polymers, supramolecular gels, liquid crystals, Langmuir monolayers, Langmuir–Blodgett films, self-assembled monolayers, thin films, layer-by-layer structures, breath figure motif structures, two-dimensional molecular patterns, fullerene crystals, metal–organic frameworks, coordination polymers, coordination capsules, porous carbon spheres, mesoporous materials, polynuclear catalysts, DNA origamis, transmembrane channels, peptide conjugates, and vesicles, as well as functional materials for sensing, surface-enhanced Raman spectroscopy, photovoltaics, charge transport, excitation energy transfer, light-harvesting, photocatalysts, field effect transistors, logic gates, organic semiconductors, thin-film-based devices, drug delivery, cell culture, supramolecular differentiation, molecular recognition, molecular tuning, and hand-operating (hand-operated) nanotechnology.  By analogy with computational optimizations of efficiency and function of novel cyber technologies such as machine learning –5, the oFurther to historical efforts in organic synthesis and materials’ fabrication, functional optimization of materials involving structural regulation at the atomic/molecular scales and nanometre scales has been widely investigated and has been accompanied by the corresponding developments in observational techniques and instrumentation on those length scales –39. WhilEmergence of the nanoarchitectonics concept can be seen as an inevitable event in the development of science and technology and its historical background can be traced in the scientific discourse . The impAccording to Masakazu Aono, the guidelines of nanoarchitectonic strategies can be summarized as follows  63]:  dyes . Hard ta3+ ion. A similar strategy can be applied to the other heavy metal ions .For site-selective modification of inorganic nanostructures, Lvov and co-workers demonstrated site-selective modifications of clay nanotube through self-assembly-type nanoarchitectonics using Schiff base binding . Clay naSelf-assembly processes can be usually conducted under ambient and mild conditions, which makes the processes appropriate for nanoarchitectonics of biological materials –118. MatIn their recent review article, Sawada and Serizawa discuss the use of M13 phage, one of the filamentous viruses, as soft matter units . Upon geSelf-assembly processes are highly influenced by the surrounding media. Ichikawa and co-workers reported lyotropic liquid crystalline behaviours of amphiphiles in dication-type amino acid ionic liquids . Nature Langmuir monolayers on water would be powerful media to regulate two-dimensional self-assembly –128. KraNote that unexplored research issues still remain in self-assembly sciences. Theoretical investigations together with experimental demonstrations have been made for further understanding on the regulations and controls of self-assembly processes and resulting complexes. Hiraoka and co-workers have been systematically investigating the importance of complementary hydrophobic molecular surfaces for precise molecular association to form discrete self-assembled complexes upon quantitative analysis of self-assembly process . Even thRecently, self-assembly processes have been re-evaluated for their dynamics. Formation of supramolecular polymers –133, pol3.Following the above-mentioned brief summary of recent trends of self-assembly research, various topics of self-assembly systems are explained below according to certain classifications. In the first category, recent examples of self-assembly systems in three-dimensional media are described.3.1.3.1.1.60 alkyl ammonium cations. Binding of fluoride anion to the oxoporphyrinogen core through hydrogen bonding efficiently promotes the ultrafast charge separation from the singlet excited state of oxoporphyrinogen to C60. The charge separation can be maintained through conversion of electron-accepting oxoporphyrinogen unit to a donor by binding with fluoride anion. This nanoarchitected complex can act as a molecular transistor.Self-assembly is a highly versatile methodology to nanoarchitect functional systems from various small units. Sizes of the nanoarchitected systems vary from molecular-level complex to material-size objects. As a small self-assembled functional unit, Hill, D’Souza, and co-workers reported self-assembled molecular complex where photoinduced charge separation is achieved through fluoride-ion binding  137]. I. I137]. Similarly, self-assembled structures often provide unusual physical effects such as regulation of electronic properties. Intramolecular structure of a single molecule can be developed into intermolecular holes through self-assembly as seen in an example demonstrated by Rathore and co-workers . Two-dimCook and co-workers prepared a self-assembled cofacial cobalt porphyrin prism that acted as a catalyst for the oxygen reduction reaction . The cobDynamic conformational changes upon self-assembly can tune chiral sensitivity around transition metal complexes as demonstrated by Shinoda and co-workers . In thisn-dodecyl)--tetrabenzo-5,6,7,12,13,14-hexaazapentacene as a small molecular unit -metallopeptide in water . This moPhoto-controllable formation and deformation of supramolecular polymer was reported by Chen, Wang, and co-workers , who useN-terminal hydrophobic groups were synthesized and self-assembled into transmembrane channel structures within lipid bilayer membranes. Selective transmembrane transport properties of Cl ions were confirmed by planar bilayer conductance measurements. Cell-membrane-like assembled structures such as vesicles and structured micelles can be nanoarchitected from various small molecules. As an interesting example, Knight, Hawker, and co-workers reported morphologically shifting spherical assemblies upon metal coordination to assemblies of hybrid peptide–polymer conjugates  photodimerization upon metal–organic assembly with Ag(I) ions. Through this one-dimensional coordination polymer route, cycloaddition reaction to pyridyl-functionalized cyclobutane can be stereo-selectively achieved with quantitative yield. Precise structure formation upon coordination-based self-assembly can be used for selective encapsulation of guest molecules. Selective capture of C60-indene or C60-anthracene bisadducts by cubic cage assembled from 4-fold-symmetric porphyrins with Fe(II) or Zn(II) was reported by Nitschke and co-workers . B. B165]. ω-norbornenyl macromonomer and norbornenyl comonomers as diluents including polystyrene, poly, and polydimethylsiloxane macromonomers. On the basis of differences of reactivity ratios between macromonomer and diluent controls, the backbone sequences as blocky, gradient, or random as well as graft diblock polymer structures with tapered, uniform, and inverse-tapered molecular shapes. Self-assembly of these polymers creates nanoarchitectonic structures with different lamellar periods and domain thicknesses. Because of general applicability of the proposed method, it can be applied for various types of polymer materials and their assemblies possibly for future extensive customization.Grubbs and co-workers reported nanoarchitectonics strategy for the self-assembly of polymers having tailored distributions of grafting sites by grafting in ring-opening metathesis polymerization . SequencHigh capability in flexible structural shifts of polymer and oligomer self-assemblies can be used for various applications such as stimuli-responsive functions. Chu and Ravoo demonstrated preparation of hierarchical supramolecular hydrogels through peptide self-assembly with photo-controlled guest release functions, using Fmoc-RGDS peptide functionalized with photo-switchable arylazopyrazole . Self-asSite-specific arrangements of responsive polymer self-assembly can be used for macroscopic actions as seen in actuator functions of surface microscopic gel arrays reported by Morin and co-workers . They pr3.3.2 . M. M2 nano3.4.2.Although pairing specificity of recognition may be slightly inferior to DNA, peptides and proteins surely have high capability of self-assembly mainly through hydrogen bonding. Accumulation of hydrogen bonding upon well-designed three-dimensional nanoarchitectures often creates surprisingly precise recognition sites –209. PepPreparation of protein–metal–organic frameworks (protein–MOFs) was demonstrated by Tezcan and co-workers who could self-assemble the metal ions presented at the vertices of the ferritin nodes in the presence of dihydroxamate linkers in synthesis of protein–MOF structures . The forWell-designed peptide segments are also good components for self-assembly. Kimura and co-workers investigated self-assembled morphologies of various types of amphiphilic block polypeptides having aromatic groups at the C-terminal of a Leu-based hydrophobic helical block and actually confirmed self-assemblies into various structures such as distorted sheet, saddle-like sheet, micelle, and so on . Upon heCoacervate microdroplets are expected to be used for synthetic cell applications but they have to be stabilized with an appropriate coating. Williams, van Hest, and co-workers proposed the use of biodegradable triblock copolymer (terpolymer) with poly(ethylene glycol), poly-(caprolactone-gradient-trimethylene carbonate), and poly-(glutamic acid) segments . The des4.Interfaces such as solid surfaces, liquid–liquid interfaces, and gas–liquid interfaces are the media that cause various interesting effects in molecular interactions and self-assembly behaviours as compared with those observed in bulk solution media –217. In 4.1.2 groups was selectively covalently modified with fluorescent dye having a succinimidyl ester moiety, 5-(and-6)-carboxytetramethylrhodamine succinimidyl ester. The latter modification of fluorescent dye can be performed without significantly damaging the preformed Ag patterns. The reported technique would be useful for printed electronics. The same research group has also demonstrated photo-reactive SAM structures to create carboxyl groups upon photo irradiation . I. IN-substert-butyl-4-hydroxyphenyl)porphyrin by deposition under ultrahigh vacuum condition [Hill et al. also reported fabrication of binary molecular monolayers composed of tetrakis(4-pyridyl)porphyrin and tetrakis  232]. T. T232]. N-[(2-hydroxy-3-methyl-5-dodec-ylphenyl)methyl]-N-(carboxymethyl)-glycine disodium salt ligand on a highly oriented pyrolytic graphite . T. Tπ-gelaO-diacetyl-6-O-pheophorbide-cellulose at the air–water interface as demonstrated by Sakakibara et al. [0−S1 transition dipole moments of the chlorin Qy bands. Photocurrent activities of the rods self-assembled at the air–water interface were also confirmed.Light-harvesting cellulose-based nanorods were fabricated through the self-assembly of a regioselectively pheophorbide-carrying cellulose acetate, 2,3-a et al. . The nann-dodecylmellitic triimide was first spread at the air–water interface, which was interacted through hydrogen bonding with macrocyclic oligoamine, 1,4,7,10-tetraazacyclododecane in subphase. Transfer of the monolayer from water surface to a mica surface through simple one-touch contact resulted in the formation of regularly sized nanodisks with a defined thickness of ca. 3 nm and tuneable diameter in the range of tens of nanometres. The formation processes to give nanosized and micro-sized regular objects are composed of a series of simple procedures including monolayer spreading, mechanical compression, and surface touching where any highly clean processes such as ultrahigh vacuum are not required.Mori et al. reported conventional methods to fabricate regularly sized nanodisks through self-assembly at the air–water interface and one-touch transfer onto a solid substrate  283,284284. A moN-isopropylacrylamide) microgels reported by Buzza, Vogel, and co-workers [Colloidal objects can be self-assembled at the air–water interface as seen in anisotropic self-assembly of isotropic colloidal microspheres of polystyrene within poly ,298. Nan4.5.2.If molecular machines and nanocars are shifted to the air–water interface, direct observation of a single molecule becomes virtually impossible. Meanwhile, synchronized motions of laterally self-assembled molecular machines would be expected. The highly anisotropic nature of dynamic interfaces, such as the air–water interface, enables us to connect macroscopic motions with molecular functions  304–307307. LateOne typical example of molecular machine controlled by macroscopic mechanical motions is the mechanical regulation of capture and release of a target guest molecule by a molecular machine self-assembled at the air–water interface  310,311311. The 4.5.3.+ complex of a cholesterol-substituted cyclen, has the helicity originated by the chirality of the side arms, which is twisted to induce modified helicity and chirality upon gradual compression of their monolayer by mechanical lateral compression (d-valine is preferential to l-valine at lower pressure regions. However, recognition selectivity is reversed to l-valine preferential at higher pressure regions. Therefore, we can select the desirable chiral selectivity by mechanical receptor tuning just by simple mechanical actions. Such flexible tuning of chiral selection cannot be attained by standard method of molecular recognition and its analyses such as crystallographic characterization of host–guest complex.The same strategy can be applied to the fine tuning of molecular receptors self-assembled at the air–water interface. Enantioselective molecular receptor, octacoordinate Napression  [312,313−1 and LiCl concentration of 10 mM, binding constant for uracil derivative is 64 times larger than that for the thymine derivative. Mechanical tuning of molecular receptor within their self-assembled structures at dynamic interface can compensate huge efforts in organic synthesis to covalently construct complicated molecular receptors capable of such fine molecular discrimination.Efficient discrimination between uracil and thymine derivatives can be accomplished by molecular receptor tuning at the air–water interface although these resembling nucleic acid bases with only one methyl group difference cannot be usually discriminated even by naturally occurring DNA and RNA. In this case, cholesterol-substituted triazacyclononane molecules self-assembled at the air–water interface were used as molecular receptors  314,315315. MechThe above-mentioned methodology for molecular recognition, molecular tuning, can be regarded as a novel category of recognition mode  316–318318. As tMechanical tuning of molecular receptors at dynamic interfaces can be coupled with photochemical processes between chromophores for colorimetric reporting. For this purpose, molecular receptor having a phenylboronic acid as a recognition moiety and a carboxyfluorescein as an indicator part was designed and spread as a monolayer at the air–water interface . Prior tThe natures of molecular tuning at the air–water interface were investigated with a simple model system. Because dihedral angles of binaphthyl unit can be quantitatively monitored, an amphiphilic binaphthyl molecule, molecular pliers, as a model molecule was mechanically deformed by lateral pressure at the air–water interface . Changes4.5.4.In contrast to the above-mentioned highly dynamic functions of molecular machines and molecular receptors at interfacial environments, highly organized static and regular molecular assemblies at interfacial media are useful for another advanced functional system such as device fabrications –332. Owi5.This review article summarizes recent research examples of nanoarchitectonics of functional materials and systems. The earlier parts of this review illustrate self-assembling systems involving various components including small molecules, polymers, inorganic materials, and biomaterials. The almost unlimited variety of these self-assembled systems confirms the great versatility of self-assembling processes for the construction of functional nanoarchitectures. Then we provided examples of self-assembly research conducted using interfacial media, including self-SAMs, thin films on solid surfaces, assemblies at liquid–liquid interfaces, and Langmuir and LB films at the air–water interfaces. Specific characteristics and improvements in functionality were obtained depending on identity of the interface. Self-assembled materials share some basic characteristics. In contrast with the widely known three-dimensional media such as highly divided solution states, molecular motility and orientation in interfacial environments are restricted to two-dimensions often resulting in unusual organizations of self-assembled objects. Interfaces also provide interaction points for materials in different phases. Molecular interactions can be significantly modulated by the heterogeneous dielectric nature of some interfacial media. In cases of interfacial self-assembled systems, lateral macroscopic action can be coupled with molecular functionalities in a medium of molecular-level thickness. Molecular organizations and materials nanoarchitected using self-assembly at interfacial media, including aqueous surfaces and electrodes, are anticipated to be applied in advanced functional systems such as molecular machine controls, sensors, and devices. Therefore, the increasing contributions of interfacial environments and their structural characteristics will have a significant influence on the future research direction of self-assembly-coupled nanoarchitectonics.Aspects of self-assembly-based nanoarchitectonics can be found in biomolecular evolutionary processes. It is obvious that interfacial environments such as cell surfaces, protein interior surfaces, and bio-macromolecular interfaces play critical roles in the functional expression of self-assembled bio-systems. Therefore, an idea of what might be achieved utilizing self-assembly nanoarchitectonics in artificial systems can be found in current biological systems. Several characteristics of highly evolved biological systems have not yet been fully accomplished using self-assembled nanoarchitectonics; for example, establishing highly sophisticated functional relays in self-assembled systems. As can be found in photosynthetic systems and signal transduction systems, highly efficient and directionally specified relays for function, signal, information, energy, and electron transfers operate as conventional actions in biological systems on the basis of highly evolved molecular dispositions in biological self-assemblies. Such high levels of organization have not been fully realized in artificial self-assembly nanoarchitectonics. Nanoarchitecting such high-level functional organizations is one of the future goals. Another potentially useful feature of biological systems is the selection and molecular ‘decision-making’ based on harmonization between multiple sometimes ambiguous responses. Beyond the precise relay of information, summation of ambiguity can result in circumstance-considered selection of outputs. This feature has not been accomplished in artificial self-assembled systems and it is a significant target to be accomplished using self-assembly nanoarchitectonics in the near future.Biological systems and nanoarchitectonics, in turn, have crucial common features . On a scNanoarchitectonics based on self-assembly relies both on exploration of chemical designs ,336 and


Kounis syndrome (KS) has been described as the coincidental occurrence of acute coronary syndromes during an allergic reaction with cardiac anaphylaxis. It is caused by inflammatory mediators released after exposure to drugs, food, environmental and other triggers. Oxidative stress occurring in various inflammatory disorders causes molecular damage with the production of advanced oxidation products (AOPPs) and advanced glycation end products (AGEs).Markers of oxidative stress were evaluated in a patient who had experienced KS after antibiotic administration in order to investigate the possible role of these molecules in KS. No data, up to now, are available on biomarkers of oxidative stress in patients with drug-induced KS.AOPPs, but not AGEs, were significantly increased in the KS affected patient compared to controls as already reported in mastocytosis affected patients. Kounis syndrome (KS) is characterized by a concurrent occurrence of acute coronary syndromes with conditions associated with mast cell activation including allergic or hypersensitivity and anaphylactic or anaphylactoid insults .The mechanism of KS consists in the release of inflammatory cytokines through mast cell activation, causing cardiac anaphylaxis with coronary artery vasospasms and/or atheromatous plaque erosion or rupture.Type I refers to patients with normal coronary arteries who develop coronary vasospasm after an allergic event. In these patients, electrocardiographic changes are secondary to ischemia. However, cardiac enzymes may or may not be normal.It is thought that this variant could be a manifestation of endothelial dysfunction or microvascular angina.Type II includes patients with a pre-existing coronary disease, in whom an allergic insult leads to plaque erosion or rupture, culminating in acute myocardial infarction or coronary vasospasm with normal cardiac enzyme and troponin levels.Type III includes patients with coronary artery stent thrombosis secondary to allergic reaction.Three different variants of KS have been defined , 3:Type We report on a case of KS, occurred after antibiotic administration, that we investigated for serum levels of oxidative stress markers such as advanced oxidation protein products (AOPPs) and advanced glycation end products (AGEs) in order to evaluate the possible role of these molecules in KS.Oxidative stress is involved in many inflammatory and immunologic diseases and it is the result of imbalance between endogenous production of free reactive oxygen species (ROS) and reduction of antioxidant defense mechanisms. As much as we know the formation of AGEs and AOPPs might be augmented in diseases linked to immune dysregulation. Among these, allergic pathologies are some of the most investigated . MoreoveNo data are available on biomarkers of oxidative stress in patients with drug-induced KS.A 73-year-old woman, a smoker, with hypertension, dyslipidemia, post-surgical hypothyroidism, cancer of the bladder treated with excision, who had previously tolerated exposition to cephalosporines, presented with flushing, skin erythema, itching to the hands and lips, and chest pain after receiving ceftriaxone i.v. for a routine cystoscopy. Emergency treatment with Hydrocortisone 500 mg i.v. and chlorpheniramine 10 mg i.m. was administered. An electrocardiogram (ECG) was performed at the Emergency Unit which showed an acute myocardial infarction while cardiac enzymes and coronary angiography were normal revealing a case of type I drug-induced KS.After experiencing antibiotic-induced KS, cardiovascular risk factors were evaluated; the patient was asked to stop smoking and statin anti-cholesterol treatment was prescribed.Risk factors for anaphylaxis were evaluated such as serum basal blood levels of tryptase, total serum Immunoglobulin E levels and serum Immunoglobulin E measurements to beta-lactam antibiotics.A serum sample was taken from the patient and also from 8 healthy volunteers as control.g for 15 min and stored at − 80 °C until used.Written informed consent, according to the declaration of Helsinki, was obtained from the patient and controls. Blood was withdrawn from the antecubital vein and allowed to clot at room temperature for 2 h. Serum was separated by centrifugation at 1200AOPPs and AGEs were measured by spectrophotometric and spectrofluorimetric methods, respectively.For AOPP determination, 200 µl of blood serum was diluted 1:5 with phosphate-buffered saline (PBS) (pH 7.4- mM 0.01 M) and 200 µl of chloramines T (0–100 mol/l) for calibration, and 200 µl of PBS as blank were applied on a micro-titer plate. Ten microliters of 1.16 M Kl and 20 µl of acetic acid were added, and absorbance at 340 nm was measured immediately. The serum concentration of AOPPs was normalized to the total protein amount determined by the Bradford assay and expressed as chloramine nmoles/mg of protein.For AGEs determination, serum was diluted 1:5 with PBS (pH 7.4- mM 0.01 M), and fluorescence intensity was recorded at maximum emission (∼440 nm) upon excitation at 350 nm and expressed in arbitrary units (AU). The serum concentration of AGEs was normalized to the total protein amount determined by the Bradford assay and expressed in AU for protein gram.Each sample was analyzed in triplicate both for AOPPs and for AGEs determination.P < 0.05.Data are expressed as medians. Differences between groups were analyzed by Mann–Whitney test. Correlation between two variables was evaluated with Spearman’s rho. The statistical analysis was performed with SPSS for Windows (version 17.0). The level of statistical significance was set at Serum basal blood levels of tryptase resulted within normal range (3.32 ng/ml), total Immunoglobulin E levels were 175 Ul/ml and serum Immunoglobulin E measurements to beta-lactam antibiotics, Penicilloyl G and V were 2.25 KU/l and 1.83 KU/l respectively.As reported in Table KS is quite rare, maybe underestimated, and can be fatal. Patients with an allergic reaction and concomitant symptoms which could suggest cardiac anaphylaxis, should be monitored from both an allergic and cardiologic point of view, taking into account a possible case of KS .KS was first described by Kounis and Zavras in 1991 as an allergic angina syndrome .It was then reported that KS is not limited to angina symptoms, but could progress to acute myocardial infarction and stent thrombosis with occluding thrombus, caused by inflammatory mediators such as histamine, platelet activating factor, cytokines, chemokines, tryptase, chymase, prostaglandins and leukotrienes .Mast cells identified in human heart, between myocardial fibers, in the perivascular tissue, in the adventitia and arteria intima, express high affinity immunoglobulin E (IgE) receptors (FcεRI). The involvement of these mediators might explain the role of cardiac mast cells in systemic and cardiac anaphylaxis. Thus, the human heart can be considered a site and target of anaphylaxis .It has been shown both in vitro and in vivo that oxidative stress activates mast-cells’ degranulation. Experimental data on the oxidative status of mice heart after cardiac anaphylaxis, induced by the injection of ovalbumin in the aortic cannula, showed that nitric oxide (NO) may not be the only influential mediator of redox changes in cardiac anaphylaxis . MastocyThe results obtained by the serum analysis of the KS affected patient seem to indicate a sort of analogy between Mastocytosis and KS; in fact, the higher levels of AOPPs compared to controls confirm once more the fundamental role acted by mast cells in KS. In what measure and how frequently oxidative stress is associated to this syndrome it has to be clarified. Basing our future researches on these results, it could be a reliable objective to investigate a wider number of samples obtained by KS patients. Both AGE and AOPP Fig. , augment


A comparative data of two generally accepted mass techniques for estimate the molecular weight distribution of polysaccharides are presented. The data were obtained from agave fructans samples of different molecular weight, which were analyzed authored independently. The two analytical techniques used were Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-ToF) and High Pressure Size Exclusion Chromatography (HP-SEC). The data set here are related to the research paper entitled “Size-exclusion chromatography (HPLC-SEC) technique optimization by simplex method to estimate molecular weight distribution of agave fructans” by Moreno-Vilet et al. [1]. This article present the comparative figures as histograms obtained from mathematically processed MALDI-ToF spectra and HPLC-SEC chromatograms. And also, the calculated polymer parameters as number and mass molecular weight, number and mass average degree of polymerization and dispersity index. The HPLC-SEC system consisted of a 1220 Infinity LC System HPLC coupled with a refractive index detector . An Ultrahydrogel DP column and guard column  was used as stationary phase, with size exclusion range from 100 to 5000 Da. The volume of the injected sample was 50 μL. As a mobile phase, deionized and bidistilled water was used at pH of 5.4, adjusted with NaOH or HCl, it was degassed and filtered through a 0.45μm PES membrane. It was operated at 0.36 mL/min and 61.7 °C of column temperature. The system was calibrated with molecular weight standards: dextrans of 1000 Da, maltoheptaose DP7, maltohexaose DP6, maltopentaose DP5, maltotetraose DP4, nystose, 1-kestose, sucrose and fructose. ChemStation with Agilent OpenLAB software were used for controlling the system operation and data analysis, according to the methodology described by Moreno et al. Samples were diluted in deionized and bidistilled water at 5 gL2.3m/z range from 800 to 20 000 and in the reflectron positive mode (200 laser shots) in the m/z range from 50 to 5000. A pepmix calibration kit  and apomyoglobin were used for calibration. The instrument was controlled using Flex Control 3.3 software  and spectra were processed using FlexAnalysis 3.3 software from Bruker Daltonics. Two acquisitions were performed for high and low mass (Linear and reflectron mode) in order to improve visualization of higher DP peaks without interference from lower mass peaks that can dominate the spectra if they are included in the same acquisition.MALDI-ToF-MS spectra were carried out on an UltraFlex I mass spectrometer  equipped with a 337 nm nitrogen laser and a grid-less delayed extraction ion source. Samples were dissolved in water in the mg/mL range and the matrix 2,5-dihydroxybenzoic acid was prepared at 10 mg/mL in a solution of 1:1 methanol:water. Samples (0.5 μL) and matrix solution were spotted and air-dried on a stainless-steel plate. Spectra were acquired in the linear positive mode (200 laser shots) in the 2.4The molecular weight distributions of the different unknown samples of agave fructans were calculated by the classical polymer parameters by the following equations:Number-average molecular weight:n is the differential area under the curve in the HP-SEC chromatogram or absolute intensity (I) of the MALDI-ToF spectra and M the molecular weight of the fraction i.Weight-average molecular weight:Dispersity index:Number and weight average degree of polymerization of fructans:


With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions. In recent years, machine learning has been actively adopted to accelerate the discovery of novel molecules with desired properties –4. WhileThe main consideration is how to represent molecules to be processed within a model and to be generated by a model. Various types of molecular representations can be considered. Among them, most existing studies have employed a simplified molecular-input line-entry system (SMILES)  to repreSMILES representation has proven useful in learning generative models for molecular design tasks owing to its simplicity. However, several limitations hinder the generation of chemically valid molecules. This representation cannot fully represent the entire chemical space, but only a part of the chemical space can be represented as SMILES strings. It is inadequate to capture the similarity between molecules, because a small perturbation in a molecule may result in a significant change in the representation. To address such limitations, numerous research attempts have been made to generate molecular graphs directly using generative models. A molecular graph With recent advances in graph neural networks –27, variomodakis  presenteomodakis  sidestepomodakis –33. ThesThis work focuses on the non-autoregressive approach for fast and efficient generation of molecular graphs. Here we propose an efficient learning method to train a VAE that generates molecular graphs in a non-autoregressive manner. To improve the molecular graph generation, we train the VAE by incorporating three additional learning objectives: approximate graph matching, reinforcement learning, and auxiliary property prediction. The trained VAE can almost always generate chemically valid molecular graphs with improved diversity compared with existing non-autoregressive methods. The proposed method also allows constrained generation of molecular graphs with specified target properties.m nodes, where i-th atom, p-dimensional vector formed by concatenating three one-hot vectors indicating the atom type, formal charge, and number of explicit hydrogens. The dimension p depends on the dataset used. For the bond between the i-th and j-th atoms, q-dimensional one-hot vector associated with the bond type. We kekulize the molecule for simplicity so that the only bond types to consider are single, double, triple, and none, hence In this work, we use the molecular graph representation defined as follows. A molecule is represented by an undirected graph We construct a conditional version of the graph VAE  in a nonm nodes and The distributions k (MPNN) , which iThe original learning objective of the VAE is given with respect the parameters nt}O(m4) .To make the learning more efficient, we introduce an approximate graph matching procedure, which aims to alleviate the computational burden for the reconstruction loss. Additionally, we incorporate reinforcement learning and auxiliary property prediction into the training of the VAE to further improve the generation performance. Details regarding the learning objectives utilized in this work are presented in the following subsection.The reconstruction loss Assuming that each edge vector is represented as a four-dimensional vector as R. The reward function returns 1 if the probabilistic graph can be decoded into a chemically valid molecular graph and 0 otherwise. In this work, the chemical validity of a molecular graph We further improve the VAE via reinforcement learning with the aim of generating chemically valid molecules. We adopt a deterministic policy gradient framework, in which the decoder of the VAE is regarded as a policy network that takes the two vectors R. Because the reward function is non-differentiable, it cannot be incorporated directly into the learning procedure. We build a reward network r that approximates the reward function R. The reward network is modeled as an MPNN with a sigmoid output. It takes a probabilistic graph as input and predicts its actual reward value. The reward network can backpropagate the VAE. We train the VAE to generate a probabilistic graph towards maximizing the output of the reward network.We wish to optimize the molecular graph generation of the VAE for maximizing the external reward function r is trained to minimize For the learning objective, we derive two additional losses R for constrained generation. For example, we can define a blacklist of undesired substructures and make the value of the reward function 0 when its input contains any substructure in the blacklist. This prevents generated molecules from having undesired substructures.It should be noted that we can impose extra domain-specific constraints regarding structures or properties on the external reward function l linear outputs. It learns from the training dataset to predict the property vector Augmenting a generative model with side information has known to improve the quality of generated samples as well as the stability of model training , 35. We R are incorporated into the first loss f is trained to minimize For learning with auxiliary property prediction, we derive two additional losses r, and predictor network f. The full learning objective combines the vanilla objective of VAE  of the three metrics for an overall comparison among the models.The performance of each model for molecular graph generation was evaluated with regard to three metrics: All the experiments were implemented based on GPU-accelerated TensorFlow in Python.Table The proposed model exhibited the highest G-mean for both the QM9 and ZINC datasets, as the model achieved good performance for all three metrics. The proposed model exhibited a validity score of > 90% for both datasets, indicating that it almost always generated chemically valid molecules. Additionally, it generated more diverse molecules, as evident from its comparable uniqueness and novelty scores. In the case of ZINC, all the compared models yielded novelty of 1, indicating that all the generated molecules were not included in the training set. The baseline models yielded relatively good performance for QM9 but rarely generated valid molecular graphs for ZINC.Compared with the ablated models, reinforcement learning helped the proposed model generate chemically valid molecules, as evident from the observation that model A2 was superior to model A1 in terms of validity. The auxiliary property prediction contributed to the diversification of the generated molecules, as the proposed model exhibited higher uniqueness than model A2.Table entclass1pt{minimaValidity, Uniqueness, and Novelty of generated molecular graphs by the model. In addition, whether the model is able to reproduce the distribution of the training set was assessed with Kullback-Leibler Divergence (KLD) and Fréchet ChemNet Distance (FCD). For baselines, four SMILES generation models  and one molecular graph generation model (GraphMCTS) were compared, as implemented in [We further investigated the effectiveness of the proposed method with the distribution-learning benchmarks in the GuacaMol framework . The benented in .The results for the distribution-learning benchmarks are shown in Table While the non-autoregressive approach has the advantage of fast and computationally efficient generation of molecular graphs without any iterative procedure, the existing methods suffer from low performance. To overcome this limitation, we proposed an efficient learning method to build a graph VAE that generates molecular graphs in a non-autoregressive manner. In order to improve the generation performance, we introduced approximate graph matching, reinforcement learning, and auxiliary property prediction into the training of the model. Experimental validation using QM9 and ZINC datasets demonstrated the effectiveness of the proposed method. Compared with existing methods, the proposed method exhibited higher performance for molecular graph generation with a validity score of > 90% as well as improved diversity of the generated molecular graphs for both datasets. We also demonstrated that the proposed model can conditionally generate novel molecular graphs satisfying specified target conditions.We believe that the proposed method can serve as an efficient tool for the discovery of new materials. Successful application of the method will allow automatic design of desired chemical structures with targeted conditions by learning implicit knowledge from data without explicit knowledge from human experts, thereby meriting further investigations. The generated molecular graphs can be examined further to obtain realistic chemical structures with desired properties.One downside of the proposed method its high complexity with regard to space and time. Both the space and time complexity increase with the size of the graphs, owing to the use of graph neural networks. Thus, the proposed method would be impractical for learning and generating large molecular graphs, e.g., hundreds of heavy atoms in a molecule. In the future, research will be performed to reduce the complexity for generating larger molecular graphs efficiently.


The authors present a method to interconnect the Visualisation Toolkit (VTK) and Unity. This integration enables them to exploit the visualisation capabilities of VTK with Unity's widespread support of virtual, augmented, and mixed reality displays, and interaction and manipulation devices, for the development of medical image applications for virtual environments. The proposed method utilises OpenGL context sharing between Unity and VTK to render VTK objects into the Unity scene via a Unity native plugin. The proposed method is demonstrated in a simple Unity application that performs VTK volume rendering to display thoracic computed tomography and cardiac magnetic resonance images. Quantitative measurements of the achieved frame rates show that this approach provides over 90 fps using standard hardware, which is suitable for current augmented reality/virtual reality display devices. Volumetric images are available from different modalities such as computed tomography (CT), magnetic resonance (MR), or 3D ultrasound (US). Four-dimensional (3D + time) image sets extend this to show function over time, e.g. blood flow or cardiac motion. These data are primarily viewed using 2D screens, which places a limitation on depth perception and understanding of the true 3D nature of the data.www.oculus.com) and HTC Vive (www.vive.com). AR headsets are mostly used by developers, e.g. Meta 2 (www.metavision.com) and Hololens (Microsoft Corp). Fish tank displays include zSpace (www.zspace.com) and Alioscopy . Progress in display technology enables natural and intuitive interaction in virtual and augmented environments, e.g. gesture and eye tracking, haptic feedback, and so on. These advanced displays and interaction tools are now widely used in the video game industry, and are slowly penetrating into other sectors such as design, marketing, and medical applications.Recently, there has been a significant boost in virtual reality (VR) and augmented reality (AR) displays, mainly in two forms: head mounted and fish tank displays. Head-mounted VR displays are popular as their price has come down to consumer level, e.g. Oculus Rift . Unity is primarily used for video game development and its popularity is due to good support, fast prototyping capabilities, and compatibility with most commercially available VR/AR displays and interaction tools. Unity has also been used to develop medical applications, mainly surgery simulators . Howeverwww.vtk.org) is a de facto standard. VTK is used in many medical imaging software, e.g. Paraview, ITKSnap, MITK and 3D Slicer.Various visualisation libraries are specifically designed for medical imaging, among which the Visualisation Toolkit , add/remove a VTK prop to/from the scene, set a VTK prop's transform , adjust the volume render transfer function, and add a cropping plane to the volume. These functions all directly relate to VTK functionality and are flexible enough for a variety of scenes to be created, manipulated, and explored. Unity and VTK multi-thread many operations, so many of these commands are queued to be safely processed at the render event.std::map of handles linked to the vtkProp3D's pointers in order to manage the scene. When a prop is added to the scene, the plugin returns its handle to be stored as a member variable by the script. The handle is then used by the script to update a prop's position or delete it. The handle is simply an index which is incremented each time a prop is added to the scene.The plugin keeps a z-axis flip in the vtkProp3D objects. This affects the volume as a left-right flip, solved by reversing the data along z as part of the loading process.There are differences between Unity and VTK which we address. First, Unity's unit system is metres, so we scale VTK volume data specified in millimetres to be in metres. Second, Unity uses a left-handed coordinate system while VTK uses a right-handed one. In practice, the camera matrices and actor locations are both reversed so the effects are limited to a 3.2In the right OpenGL environment, VTK external rendering will work with little effort. However, OpenGL Core pipeline implementations vary as their configuration is determined by the author. Differences between Unity's OpenGL pipeline and VTK's expectations of it have led us to make modifications to VTK for volume rendering and camera updates to work in Unity. These differences are not due to defects in either VTK or Unity. Rather, they are the result of different design decisions made by the two development teams.vtkExternalOpenGLRenderer obtains the camera view and projection matrices from OpenGL and sets them in the vtkExternalOpenGLCamera. This works for a legacy OpenGL pipeline, where these matrices are stored in a specified way. However, it does not work for the OpenGL Core pipeline in Unity where the matrices are not stored in the way VTK expects.By default, the vtkExternalOpenGLRenderer so the camera view and projection matrices can be set. These are obtained from the Unity camera, and passed in through the plugin. The original code where they are obtained directly from OpenGL was removed. After setting these view and projection matrices in the camera the existing VTK recalculation of the camera up vector and position is performed, required for the camera to work correctly.We add methods to GL_BACK_BUFFER, and at the start of the volume rendering copies a depth image from the GL_DEPTH_BUFFER for use in ray termination. Unity uses frame buffer objects (FBO) with textures attached for colour, depth, and so on. As a result, the depth image is not stored in the standard buffer as VTK expects. We address this by obtaining the name of the current FBO's depth texture, and use this as the source of the copy operation. We modified vtkOpenGLGPUVolumeRayCastMapper to copy from the FBO depth texture, requiring an additional method in vtkTextureObject to enable a depth image to be copied from a texture. Fig. These changes are sufficient for VTK opaque surface rendering in Unity, and for volume rendering when none of these surfaces impinges the volume. For volumetric medical data we also require surface rendered objects, e.g. landmarks, within the volume. For the volume and surface rendered objects to be blended correctly VTK volume rendering uses the depth buffer to calculate early ray termination when there is an object inside of the volume . During We have tested these changes with opaque surface objects embedded within a rendered volume, which yields correctly blended scenes. The opaque objects may be rendered from both Unity and VTK. This configuration works because opaque surface objects are rendered before the volume and write to the depth buffer, which is in turn used by the volume renderer. On the other hand, semi-transparent objects (surface or volume) may not render correctly when combined with another rendered volume due to depth sorting problems. Particularly, rendering multiple volumes will pose depth sorting problems. A potential solution is to render all of the semi-transparent objects in VTK and allow VTK to perform depth sorting and, if necessary, depth peeling. We have not tested other rendering features in VTK, or other OpenGL rendering pipelines, which is left as future work and might require further modifications.3.3Start event a command buffer is added to the camera which will call the plugin's render callback function. No data are passed at this call, so the camera's world-to-camera and projection matrices are obtained from the Unity camera and set in the plugin in the camera's OnPreRender event.Synchronisation of the VTK camera to the Unity one is performed by a C# script attached to the main camera in the Unity scene. In this script's OnPreRender event queries the camera to find out if it is a stereo camera. Then, if it is a stereo camera, if it is the left or right one. From this it obtains the correct view and projection matrices to set in the plugin. The command buffer call to the render event does not indicate if it is being called by the left or right camera, and this cannot easily be ascertained. We therefore make the assumption that the OnPreRender and render events are called in the same order. In the plugin this is done by pushing the view and projection matrices onto the back of a queue at an OnPreRender event, then popping them off the front of the queue at the render event, so their order is preserved. Whilst this solution may be improved, our experiments did not show any left-right camera reversal.For a stereoscopic display, e.g. a VR headset, rendering is performed for the left and right eyes. Unity represents a stereo camera as a single camera in its scene making this slightly more complex, rather than as separate left and right eye cameras where stereo rendering is trivial. The 3.4GameObject in the Unity scene, but without any mesh renderer enabled. It may have other active Unity components, e.g. a collider for interaction. A proxy has an active C# script attached to control the creation and update of a prop in VTK. These scripts are structured to fit in with the Unity GameObject lifecycle:Start event initialises the VTK propUpdate sets the VTK prop's transform, updates the window of the transfer function, and so onDestroy removes the VTK propFor example, our rendered volume is a GameObject to which we attach a collider for controller interaction, and a C# script to initialise, update, and destroy the VTK prop. The scripts actions are illustrated in Fig. Configuring and updating the scene in VTK is also performed with C# scripts. We use the concept of a proxy to enable interaction to be handled by Unity while rendering is performed by VTK. A proxy has a Similarly, a proxy object with a script attached can add and update a surface rendered primitive, e.g. a sphere. Multiple Unity entities can access the plugin functions, e.g. a script can be attached to a controller to alter the transfer function window of the volume.Volume cropping planes are somewhat more complex as they are entities themselves but also need to be associated with the volume they crop. To associate a cropping plane with a volume, the volume proxy shares its prop handle with the script which creates the cropping plane. This handle is then passed back into the plugin when the cropping plane is added to the scene so that the plugin knows which volume the cropping plane is going to crop.A cropping plane should move with the volume when the user moves the volume in the scene, but the user should also be able to move the cropping plane independently of the volume. Achieving this offers a good example of Unity's power and flexibility.The cropping plane proxy in Unity is created as a child of the volume proxy, so that when the volume moves the cropping plane's position relative to the volume is unchanged. In Unity, this works because an object's transform is relative to its parent. To move the cropping plane independently, its proxy is un-parented from the volume proxy, moved, and then re-parented to the volume proxy. Unity updates the parent–child transform automatically. Fig. Currently, we have not synchronised lighting between Unity and VTK. By default, scenes using the external VTK renderer have no lights. Therefore, props within the VTK scene are unlit.3.5We have primarily been developing with the HTC Vive and the Unity SteamVR plugin. These are well developed and the combination of controllers and lighthouses gives significant flexibility.CameraRig prefab, providing a camera which tracks the Vive headset, and tracked representations of the left and right-hand controllers. To use this, we add the CameraRig prefab to the scene and delete the original camera. To enable VTK rendering, we attach our C# script to the Camera (eye) component of the CameraRig. To create interactions using the controllers, we add interaction scripts to the Controller (left) and Controller (right) components, e.g. to pick up and place objects using the trigger, or control the transfer function windowing using the touchpad.The SteamVR plugin includes a 4http://cancergenome.nih.gov), and made up of We implemented a Unity plugin and the described VTK modifications which allow volumetric medical data to be loaded and visualised using the VTK volume renderer within a Unity application. In this work, we used Unity 2018.1 and VTK 8.1, under Windows 10 on a standard workstation . We used medical images from two modalities. First, a thoracic CT dataset obtained from the TCGA-SARC dataset  Biomedical Research Centre based at Guy's and St Thomas’ NHS Foundation Trust and King's College London and supported by the NIHR Clinical Research Facility (CRF) at Guy's and St Thomas. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The results shown here are in part based on data generated by the TCGA Research Network: 8None declared.


Focal and Segmental Glomerulosclerosis (FSGS) is a severe glomerulopathy that frequently leads to end stage renal disease. Only a subset of patients responds to current therapies, making it important to identify alternative therapeutic options. The interleukin (IL)-1 receptor antagonist anakinra is beneficial in several diseases with renal involvement. Here, we evaluated the potential of anakinra for FSGS treatment. Molecular process models obtained from scientific literature data were used to build FSGS pathology and anakinra mechanism of action models by exploiting information on protein interactions. These molecular models were compared by statistical interference analysis and expert based molecular signature matching. Experimental validation was performed in Adriamycin- and lipopolysaccharide (LPS)-induced nephropathy mouse models. Interference analysis (containing 225 protein coding genes and 8 molecular process segments) of the FSGS molecular pathophysiology model with the drug mechanism of action of anakinra identified a statistically significant overlap with 43 shared molecular features that were enriched in pathways relevant in FSGS, such as plasminogen activating cascade, inflammation and apoptosis. Expert adjudication of molecular signature matching, focusing on molecular process segments did not suggest a high therapeutic potential of anakinra in FSGS. In line with this, experimental validation did not result in altered proteinuria or significant changes in expression of the FSGS-relevant genes COL1A1 and NPHS1. In summary, an integrated bioinformatic and experimental workflow showed that FSGS relevant molecular processes can be significantly affected by anakinra beyond the direct drug target IL-1 receptor type 1 (IL1R1) context but might not counteract central pathophysiology processes in FSGS. Anakinra is therefore not suggested for extended preclinical trials. Focal and Segmental Glomerulosclerosis (FSGS) is a histopathological description for severe sclerotic lesions in the kidney glomeruli and a common cause of nephrotic syndrome characterized by proteinuria, edema, and hyperlipidemia . The etihttp://www.accessdata.fda.gov/drugsatfda_docs/label/2016/103950s5175lbl.pdf) and has recently been commissioned for treatment of additional rare periodic fevers and autoinflammatory diseases [in silico [versus disease target interference at the molecular level integrates information obtained from unbiased data mining into a disease specific molecular pathophysiology framework.Anakinra is an IL-1 receptor antagonist (IL1-RA) that blocks the biologic activity of IL-1 by competing with its binding to the IL-1 receptor type I (IL-1R1), which is expressed in a wide range of cell types. Anakinra has shown to be effective in various inflammatory diseases and is currently licensed to treat rheumatoid arthritis and cryopyrin-associated periodic syndromes (CAPS) http://wwdiseases . Experimdiseases . In linediseases –15. As tdiseases , 16, we n silico . This neIn this study, a molecular pathophysiology model characterizing the molecular processes associated with FSGS was overlapped with the molecular mechanism of action of anakinra to predict the therapeutic potential for treatment of FSGS by a workflow combining interference analysis, molecular signature matching and experimental validation –20.Protein coding genes associated with FSGS or anakinra were collected by a literature mining approach based on public domain data. A PubMed search using the respective major medical subject headings (MeSH) term as query string was performed to identify publications of relevance. Protein coding genes explicitly discussed in these publications were extracted from gene2pubmed by filtering based on PubMed ID and taxonomy ID (9606 for human).Construction of molecular process models was performed as previously described –23. In bi.e. upregulation reported with FSGS and downregulation with anakinra and vice versa) was used to assess whether anakinra was expected to reverse the FSGS specific expression and thus to counteract FSGS pathophysiology and ambiguous results were clarified by expert adjudication. Based on the assumption of identification of central aspects of FSGS pathophysiology by topological segmentation, this analysis focused on these molecular clusters, starting with those with the highest number of shared protein coding genes. Eventually contradicting data were resolved in an expert adjudication process.Matching signature of disease and drug relevant genes reported in scientific literature was used to predict therapeutic potential of anakinra in FSGS. To do so, regulation of a given gene in scientific literature associated with FSGS was compared to that reported for anakinra. Discordant regulation Luo/J x hNPHS2Cre mice (kind gift of Prof. Tobias B. Huber) [Lipopolysaccharide (LPS) nephropathy was induced by intraperitoneal (i.p.) injection of 12 mg/kg LPS  in 0.9% saline into 10 weeks old female Gt(ROSA). Huber) . All ani. Huber)  from dayGlomeruli and podocytes were isolated as described before from 3 animals/group . RNA wasUrinary albumin levels of spot urine were assessed by ELISA . Creatinine levels were measured with the Creatinine Assay Kit  according to the manufacturer’s protocols. Urine albumin-to-creatinine ratio (UACR) was calculated  / urine creatinine (g/dl) = UACR in mg/g ≈ albumin excretion in mg/day) to control for variation in urine concentration.www.panther-db.org) as previously described [Biological pathway enrichment analysis was performed for the set of protein-coding genes using the PANTHER database . Values from different groups were compared using one-way ANOVA with Tukey's honest significant difference test as a post hoc; P values lower than 0.05 were considered significant.Data mining from scientific literature extracted 225 protein coding genes associated with FSGS that were also part of the consolidated interaction network. 206 protein coding genes were identified as member of the induced subgraph, 19 had no interaction to any other feature of the FSGS data set and were therefore not included in molecular model computation. Following the network segmentation procedure aimed at identifying FSGS molecular process segments defined by topological characteristics of the FSGS-specific subgraph, the constructed molecular process model for FSGS held 64 protein coding genes in 8 process units ranging in size from 3 to 16 protein coding genes .Interfering the FSGS molecular model with anakinra´s mechanism of action gene set on the level of feature overlap  identifiPlasminogen activating cascade as top enriched of 7 molecular pathways . Among the list of enriched molecular pathways were signaling cascades linked to inflammation  and apoptosis . In addition, CCKR signaling map and Gonadotropin-releasing hormone receptor pathway were significantly enriched with more than 1 protein coding gene from the constructed FSGS molecular model.PANTHER pathway analysis  revealedMatching signature of disease and drug relevant genes, starting in the cluster with the highest number of molecular features shared between the FSGS pathophysiology model and the anakinra mode of action gene data set showed in none of these 5 genes a discordant but in two of them a concordant expression pattern between FSGS and anakinra .in silico prediction of involvement of IL-1β in the molecular pathophysiology of FSGS was supported as IL-1β was >8-fold upregulated in glomeruli of BALB/c mice with induced Adriamycin-nephropathy for 35d  context. For example, the strongest overrepresented pathway Plasminogen activating cascade suggests high relevance with regards to current FSGS pathophysiology but has not yet been associated with anakinra´s mechanism of action. The soluble urokinase plasminogen activator receptor (suPAR) is intensively discussed as circulating factor causing FSGS [This molecular process model representation of FSGS was used to assess the potential of anakinra as therapeutic option in FSGS , 29. Sta pathway , 17. Othing FSGS , 31.in silico assessment. Qualitative in-depth analysis of scientific literature at the level of expert adjudication was used to allow prediction of the effect of anakinra on FSGS. Matrix metalloproteinases (MMPs) are regulators of ECM turnover and close regulation is essential for ECM homeostasis. MMP2 and MMP9 were found significantly elevated in the urine of patients with steroid-resistant nephrotic syndrome as compared to steroid-sensitive patients [In the next step, we focused on anakinra´s drug mechanism of action applying comparison of molecular signature at the level of shared individual protein coding genes embedded in the molecular process segments–thus reflecting drug effects on central aspects of FSGS molecular pathophysiology , 22. Thipatients . A studypatients . Interespatients . Collagepatients . Anakinrpatients  and to apatients , 38, butin silico data two independent mouse models covering different pathophysiologic aspects of FSGS, showed no efficacy of anakinra in FSGS treatment. Our validation assay has several limitations. First, levels of anakinra were neither assessed in blood nor tissue. Second, no positive control arm (such as steroid treatment) was included in the experimental design, and, third, anakinra might still be effective in other genetic mice models due to the multiplicity of etiologic factors in the pathogenesis of FSGS. However, the used dose in the mouse experiments corresponded to a more than 15-fold equivalent of the recommended human daily dose and has previously been reported to be effective [FSGS is very heterogeneous and no model covers all major disease nodes relevant in its pathophysiology , 39. To ffective . Althougin silico stage of efficacy analysis [In summary, our workflow demonstrated that a molecular network-based systems pharmacology approach might represent a rational strategy for drug repurposing in FSGS. It might thus represent a feasible approach for efficacious development of novel therapies for complex difficult-to-treat diseases such as FSGS due to early exclusion of drug candidates during the analysis .S1 TableThe table provides values behind the means, standard deviations and other measures reported.(XLSX)Click here for additional data file.


Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor. There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with the desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representations have in common in a low-dimensional representation vector. Once the model is trained, this representation can be extracted for any new molecule and utilized as a descriptor. In fair benchmarks with respect to various human-engineered molecular fingerprints and graph-convolution models, our method shows competitive performance in modelling quantitative structure–activity relationships in all analysed datasets. Additionally, we show that our descriptor significantly outperforms all baseline molecular fingerprints in two ligand-based virtual screening tasks. Overall, our descriptors show the most consistent performances in all experiments. The continuity of the descriptor space and the existence of the decoder that permits deducing a chemical structure from an embedding vector allow for exploration of the space and open up new opportunities for compound optimization and idea generation. Molecular fingerprints encode structural or functional features of molecules in a bit string format and are commonly used for tasks like virtual screening, similarity searching and clustering.extended-connectivity fingerprints (ECFPs) were introduced to model quantitative structure–activity relationships (QSAR) for biological endpoints by way of classical machine learning approaches as well as for ligand-based virtual screening (VS).Molecular descriptors play a crucial role in chemoinformatics, since they allow representing chemical information of actual molecules in a computer-interpretable vector of numbers.Recent advances in the field of Deep Neural Networks (DNNs)Following these considerations, work was also done to apply DNNs directly on supposedly more complete and lower-level representations of a molecule such as the molecular graphet al. proposed a variational autoencoderde novo molecular design, the authors showed that the resulting representations could also be used as descriptors for a down-stream classification task. Xu et al. proposed a related unsupervised approach based on sequence to sequence learning.Recently, work was also done to learn molecular descriptors in an unsupervised and data-driven way. Gómez-Bombarelli autoencoderlatent representation). The decoder network takes the latent representation as the input and aims at transforming it back to the input sequence. The whole autoencoder network is trained on minimizing the mean reconstruction error on a single-character level for each input sequence. By introducing an information bottleneck between the encoder and the decoder, the network is forced to compress the essential information of the input, so that the decoder still makes as few errors as possible in the reconstruction. If the trained autoencoder is able to encode all the necessary information of a given molecular representation to accurately reconstruct the original molecular representation, Xu et al. argue that it may also capture more general chemical information about the molecule and could be used as a molecular descriptor. However, training an autoencoder on reconstructing a sequence which represents a molecule bears the risk that the network solely focuses on syntactic features and repetitive patterns of this sequence, neglecting its semantics and failing to encode higher-level concepts such as molecular properties.Both studies use an e.g. SMILES. Since the decoder uses the encoded latent representation to generate a semantically equivalent but syntactically different representation, the network does not benefit from encoding unnecessary information about the input sequence. However, the decoder can only succeed in generating the right translation for a given molecular representation if the encoder compresses a comprehensive description of the chemical structure in the latent representation.In this work, we want to address this issue by proposing a method that is based on a translation rather than a reconstruction methodology see . Similare.g. SMILES) and transform it into a continuous and fixed-sized representation. Once trained, the resulting model can be used to extract meaningful molecular descriptors for query structures without the need for retraining or including labels. To analyse the quality of the resulting molecular descriptors, we perform a variety of experiments on predictive QSAR and virtual screening tasks. Finally, we show that it is possible to navigate smoothly in this new continuous chemical descriptor space by modifying slightly the molecular representation of an existing compound in a given direction and using the decoder to obtain new chemical structures.By training the translation model in a data-driven way on a large set of chemical structures, we propose a model that can extract the information contained in a comprehensive but discrete and variable-sized molecular representation , a one-hot representation of token Ti is defined by an N-dimensional vector with a one in the i-th entry and zeros elsewhere. We defined different lookup tables for both SMILES and InChI representations, mostly tokenizing the sequences on a character level except for “Cl”, “Br” and “InChI = 1S/”. We tokenized 38 and 28 unique characters for SMILES and InChI sequences, respectively.In order to use the aforementioned sequence-based molecular representations as the input and output of the translation model, we tokenized the sequences and encoded them in a one-hot vector representation. By defining a lookup table T for the 2.2et al., this classification model takes the latent representation of the translation model as the input and predicts certain molecular properties which can be directly deduced from the molecular structure. We fixed the classification model as a 3-layer fully connected neural network, mapping the latent space to the molecular property vector.To further encourage the model to learn a meaningful representation of a molecule, we extend the translation model by an additional classification model for certain molecular properties. Similar to the method proposed by Gómez-Bombarelli teacher forcingThe output of the decoder network's RNN is a sequence of probability distributions over the different possible characters defined in the respective lookup table. The complete model is trained on minimizing the cross-entropy between these probability distributions and the one-hot encoded correct characters in the target sequence as well as minimizing the mean squared error in predicting the molecular properties (classifier network). For the decoder RNN we utilized We monitored the translation accuracy of the model on a single-character level, by comparing the correct character in the target sequence with the most probable character in the decoder RNN's output at each position. To select the best combination of translation task and architecture, we used the predictive performance of machine learning models built on two QSAR datasets using the respective latent representations as descriptors. This ensures that the translation model not only works well at translating (high single-character accuracy) but is also well suited to extract meaningful molecular descriptors from the input sequence (good performance of a simple QSAR model build on the embedding).2.3P between –7 and 5. Additionally, we removed the stereochemistry, stripped the salts and only kept the largest fragments. For each molecule, nine molecular properties were extracted: log P, the maximal and minimal partial charge, the number of valence electrons, the number of hydrogen bond donors and acceptors, Balaban's J value,The translation model was pretrained on a large dataset composed of molecular structures from the ZINC15  and PubCFor the evaluation of the molecular descriptors extracted by the final translation model, we performed eight QSAR and two VS experiments. The QSAR datasets see  were takThe VS experiments were performed on 40 targets of the Directory of Useful Decoys (DUD) and 17 targets of the Maximum Unbiased Validation (MUV) dataset.All compounds of the evaluation datasets were removed from the pretraining dataset.2.4Our new molecular descriptors were benchmarked against state-of-the-art descriptors in QSAR and VS experiments.For modelling structure–activity relationships, we compare three different approaches: classical machine learning models applied on our descriptors and on circular fingerprints of different radii and folding as implemented in RDKit (see details in the ESI†) as well as an end-to-end molecular graph convolution method as implemented in DeepChem.The graph convolution models were trained directly on the different QSAR datasets. Hyperparameters such as learning rate and filter size were optimized in a cross-validation  and the area under the receiver operating characteristic curve (ROC AUC) for the regression and classification tasks, respectively.To select the best performing combinations, we specifically looked at the coefficient of determination . Since sequence-based representations of molecules such as SMILES or InChI are easily obtained from cheminformatics packages, the pretraining of the model can be performed on a vast chemical space . Once the pretraining is finalized, the translation model can be used to encode compounds into the embedding or to decode embeddings into compounds. The obtained compound embedding can be utilized as a new continuous and reversible molecular descriptor that can be used to evaluate similarity in chemical space or train machine learning models to predict properties and biological activities.3.1We evaluate the different network architectures of the translation model see  in termsGenerally, as the models get better at translating the input to the output sequence, the predictive performance of an SVM based on the latent representation also improves. Since the translation model is trained on producing the correct translated sequence for a given input sequence, it is forced to store all important information necessary to do this translation in the bottleneck of the network: the latent representation see . The morThe overall best performance was achieved with a translation model based on an RNN architecture for the encoder network that was trained on translating from a SMILES representation to its canonical version , making the generation of a correct InChI string for a given molecule a difficult task to learn.We also tried to train models on translating from canonical SMILES to InChI representations. These models, however, failed (in contrast to the opposite task) to learn anything. This is probably due to the higher complexity of the InChI format  that were included in the classification task.In order to assess the impact of the additional classification task of molecular properties, All models based on translating between two different molecular representations show a clear improvement over models trained on reconstructing the same input sequence. Interestingly, translating between two molecular representations seems harder to learn than reconstructing the same input representation see . This ca3.2Next, we extracted molecular descriptors of the remaining (test) QSAR datasets see  with theGenerally, the hyperparameter-optimized methods perform on a comparable level for most of the QSAR tasks, each method showing at least on one task a slightly better mean performance over the different splits.The lipophilicity and aqueous solubility datasets show the largest variance in performance between the models. The graph-convolution method outperforms the models based on the baseline fingerprint in predicting these physico-chemical endpoints. For the solubility endpoint, however, this is only true in the case of random splits. In the case of cluster splits, the graph-convolution model apparently fails to generalize on the hold-out clusters. This is probably due to the relatively small size of the solubility dataset. Since the graph-convolution method is trained end-to-end, it has to learn to extract meaningful features for each dataset from scratch which could lead to overfitting, if training data are limited. In contrast, the baseline fingerprints and our descriptors are built upon predefined or pretrained feature extraction methods respectively, independently from the task at hand. Our proposed molecular descriptors show good performance in predicting physico-chemical endpoints  even in the cluster cross-validations on the small datasets (solubility and melting point).Summing up, our proposed molecular descriptors exhibit competitive or better performance than the best baseline models in all investigated QSAR tasks.Additionally, we would like to emphasize that we fixed our feature extraction method based on two datasets (Ames and lipophilicity on random splits) to avoid a model selection bias on the remaining test sets. This, however, was not done for the baseline methods. The fingerprint-based models could choose between nine different flavours of circular fingerprints and three different learning algorithms for each task respectively and due to the considerable training time the graph-convolution models were not trained in a nested cross-validation. Hence, it is remarkable that, although we applied a much harsher evaluation scheme on our method, it still achieved comparable – if not better – results to the baseline methods.3.3The goal of ligand-based virtual screening (VS) is to rank a large set of compounds with respect to their activity on a certain target based on their similarity to some known active query compounds. It is based on the assumption that similar compounds will have a similar biological activity.et al. to compare our extracted descriptors against other state-of-the-art molecular descriptors. In p < 0.05). Thus, similarities measured between compounds in our proposed descriptor space are better correlated with their pharmacological similarity than similarities measured in the baseline fingerprint spaces.To investigate how well our descriptors are suited for ligand-based virtual screening, we followed the benchmark protocol of Riniker Interestingly, the best baseline descriptor in the DUD screen  is only fifth in the MUV screen. The best baseline descriptor in the MUV screen (ap) is not even represented in the top ten performing descriptors in the DUD screen. Our descriptor, however, shows robust performance over all analysed targets see , even th3.4et al., a continuous encoding of a molecular structure enables us to explore the neighbourhood of this molecule by decoding from points close to the query molecule's embedding.As opposed to the previously discussed baseline fingerprints, our proposed descriptor is continuous and the encoding into the descriptor space is reversible, due to the decoder part of our translation model. This opens new possibilities in terms of compound optimization and exploration of the chemical space. As already shown by Gómez-Bombarelli r between the compound's molar weight and the respective step along the first principle component was r = 0.9470 (p = 0.00048). The mean correlation between the compound's partition coefficient log P and the respective step along the second principle component was r = –0.916 (p = 0.00015). These results suggest a general correlation between shifts in certain directions in the descriptor space and certain molecular properties.In All analysed points along these two axes, when decoded, resulted in a valid SMILES (interpretable by RDKit). To further investigate how well our model is suited to explore around arbitrary molecule representations in arbitrary directions, we iteratively moved along 100 random directions for 1000 randomly picked compounds, respectively  distance .et al. demonstrates that a simple RNN solely trained on generating SMILES sequences (no encoder/decoder framework) can obtain similar high valid SMILES ratios of 96% with random sampling.52In a similar study Blaschke 4We proposed a novel methodology that is able to learn to extract meaningful molecular descriptors, solely by an unsupervised training on a large dataset of molecular structures. We showed that the molecular descriptors extracted by our method significantly outperform state-of-the-art molecular fingerprints in ligand-based virtual screening (VS) experiments. Moreover, we show that machine learning models based on our descriptor perform similarly – if not better – on various quantitative structure–activity relationships (QSAR tasks), when compared to multiple state-of-the-art molecular fingerprints and computationally expensive graph-convolution models. Generally, our proposed descriptors show, compared to the baseline methods, consistent performance in all experiments, even across different experimental concepts such as QSAR and VS. We believe that our method combines the advantages of both baseline models. Our method does not depend on fixed feature extraction rules but learns its own extraction method in a data-driven way. However, since it is pretrained on a large set of molecules, the resulting features generalize well and are less prone to overfitting.As we focused in this work on translating between different string-based molecular representations, an evident follow-up would be the translation of conceptually different molecular representations such as the molecular graph or 3D-structure-based representations like the van der Waals and/or electronegative potential surface.Since our proposed molecular descriptors are continuous and can be translated back into a valid molecular structure, they open new possibilities in terms of compound optimization and navigation of the chemical space. We observe smooth and meaningful transitions in the chemical structure when a molecule's embedding is shifted in certain directions, where shifts along different axes in our descriptor space correspond to different structural and functional properties in the chemical space.et al. already showed that their autoencoder framework could be utilized to automatically design molecules with respect to multiple properties such as synthetic accessibility and drug-likeness. Since our model's latent space was shown to be significantly better correlated with the molecule's biochemical properties, we think that our proposed translation method could significantly improve such a method's ability to generate and optimize molecules, also enabling optimization with respect to biological activity. These aspects will be explored and discussed in an upcoming study.Moreover, Gómez-Bombarelli https://github.com/jrwnter/cddd.The source code and a pretrained model (to extract our proposed molecular descriptors out of the box) are available at There are no conflicts to declare.Supplementary informationClick here for additional data file.