Selecting diverse molecules from unexplored areas of chemical space is one of the most important tasks for discovering novel molecules and reactions. This paper proposes a new approach for selecting a subset of diverse molecules from a given molecular list by using two existing techniques studied in machine learning and mathematical optimization: graph neural networks (GNNs) for learning vector representation of molecules and a diverse-selection framework called submodular function maximization. Our method, called SubMo-GNN, first trains a GNN with property prediction tasks, and then the trained GNN transforms molecular graphs into molecular vectors, which capture both properties and structures of molecules. Finally, to obtain a subset of diverse molecules, we define a submodular function, which quantifies the diversity of molecular vectors, and find a subset of molecular vectors with a large submodular function value. This can be done efficiently by using the greedy algorithm, and the diversity of selected molecules measured by the submodular function value is mathematically guaranteed to be at least 63% of that of an optimal selection. We also introduce a new evaluation criterion to measure the diversity of selected molecules based on molecular properties. Computational experiments confirm that our SubMo-GNN successfully selects diverse molecules from the QM9 dataset regarding the property-based criterion, while performing comparably to existing methods regarding standard structure-based criteria. We also demonstrate that SubMo-GNN with a GNN trained on the QM9 dataset can select diverse molecules even from other MoleculeNet datasets whose domains are different from the QM9 dataset. The proposed method enables researchers to obtain diverse sets of molecules for discovering new molecules and novel chemical reactions, and the proposed diversity criterion is useful for discussing the diversity of molecular libraries from a new property-based perspective. The size of chemical space, i.e., the number of molecules in it, is estimated to be 6, and other estimations of chemical-space sizes have also been reported7. In any case, the number of molecules is too large to explore exhaustively. Currently, more than 68 million molecules are registered in the chemical abstracts service (CAS) of Americal Chemical Society9, and some accessible online molecular databases, e.g., PubChem10, ZINC11, have been constructed. Moreover, owing to recent advances in high throughput screening, chemoinformatics12, and machine learning13, many chemical compounds have been discovered from chemical space in the fields of organic light-emitting diode14, organic synthesis15, and catalyst16. These are, however, small fractions of chemical space, and vast areas remain unexplored.Chemical space17. In medicinal chemistry, diversity selection algorithms have been widely studied for exploring chemical space and discovering bioactive molecules21. The diversity of a set of molecules is also essential in molecular library design22. Furthermore, when analyzing the quality of molecular libraries, the way to assess their diversity is crucial. This paper contributes to diverse molecular selection by proposing a novel selection framework and a new criterion for evaluating the diversity of molecules.Selecting diverse molecules from chemical space is an important task for discovering molecules that exhibit novel properties and new chemical reactions23, MACCS keys24, and Daylight fingerprints25, which typically encode structural information of molecules as binary vectors. Given such descriptors, pairwise dissimilarities are defined to quantify how dissimilar two molecules are. A widely used pairwise dissimilarity is the Tanimoto coefficient 26. Computation of molecular similarities constitutes a broad research area, and other approaches based on, e.g., graph edit distances27, cosine similarities of SMILES kernels28, maximum common substructures29, a root mean square deviation of 3D-molecular structures30 and the persistent homology 31 have also been proposed. Given such pairwise (dis)similarity measures, the diversity of sets of molecules is usually evaluated with, e.g., the mean pairwise dissimilarity (MPD) or the mean distance to closest neighbors calculated over selected molecules.For computing the diversity of sets of molecules, most existing methods start by specifying molecular descriptors, which encode molecules as vectors. Examples of molecular descriptors include ECFP32. In each iteration, the dissimilarity between a new molecule and a set of selected molecules is computed according to a certain rule, e.g., MaxSum33 and MaxMin35, and the choice of such rules affects outputs of the greedy algorithm. The diversity of molecular sets obtained by the greedy algorithm is usually evaluated with, e.g., the MPD defined with the Tanimoto coefficient of MACCS keys. Thus calculated diversity values intrinsically depend on the choice of molecular descriptors and pairwise dissimilarities. Consequently, the existing framework for selecting molecules and evaluating the diversity puts much weight on structural information of molecules since molecular descriptors usually encode structural information of molecules and pairwise dissimilarities are calculated based on such structure-based descriptors.While the diversity of molecules can be computed as above, selecting molecules that maximize a diversity measure from given molecular lists is computationally more challenging. For example, a naive brute force search for selecting 10 out of 100 compounds requires calculating diversity values 36. Also, in drug discovery, the Fréchet ChemNet Distance (FCD), which is a novel property-based metric using hidden layers of prediction models for bioactivities as representation of molecules, has been reported to be useful for evaluating models for generating molecules37. When it comes to discovering novel reactions, examining collections of molecules that are diverse regarding molecular properties  is vital for efficient exploration of chemical space. Therefore, utilizing not only structural information but also properties of molecules can be a promising approach to pushing the diverse molecular selection framework to the next level, which will facilitate the discovery of novel molecules and new chemical reactions.On the other hand, exploration of chemical space that takes the diversity of molecular properties into account has been reported to be effective for discovering novel functional materials43. GNNs transform molecular graphs into vectors, which are used in downstream property prediction tasks. Notably, GNNs generate vectors taking both molecular properties and structural information of molecules into account, and it is reported that molecular vectors obtained from trained GNNs successfully reflect chemists’ intuition of molecular structures41. Therefore, GNN-based molecular vectors can be effective alternatives to the aforementioned traditional molecular descriptors. However, to leverage GNN-based vectors for selecting diverse molecules, we need to discuss how to select diverse molecular vectors generated by GNNs, for which the existing structure-based selection framework is not necessarily appropriate.In the field of machine learning, neural network (NN) architectures have yielded great success in various areas such as image recognition and natural language processing. Following the achievements, researchers have applied them to molecular property prediction tasks. Among such approaches, graph neural networks (GNNs) have been gaining attention since many GNN-based prediction methods have achieved high performancessubmodular function maximization45. This framework is one of the best ways for diverse selection due to the following two advantages. First, many submodular functions for quantifying the diversity have been developed in various fields, and thus we can choose an appropriate one to achieve desirable diverse selection. In particular, some submodular functions can represent relationships between multiple molecules that pairwise similarities cannot capture. For example, the log-determinant function, a submodular function our method will use, serves as a volumetric diversity measure of molecular vectors. Such functions can offer us the potential for going beyond the existing pairwise-similarity-based framework. Second, and more importantly, we can mathematically guarantee the greedy algorithm to select near-optimally diverse molecules in terms of submodular function values. Specifically, resulting submodular function values are guaranteed to be at least 44. Moreover, the empirical performance of the greedy algorithm for submodular function maximization is known to be much better; it often achieves more than 47. Therefore, the submodularity-based approach enables us to efficiently obtain near-optimally diverse sets of molecules without relying on costly selection algorithms such as the brute force search.Mathematically, the problem of selecting diverse items  has been widely studied as 48, which uses GNNs in selection methods, while we use GNNs to design submodular functions. In view of this, our work provides a new type of application combining GNNs and submodular function maximization. Furthermore, to evaluate the diversity of selected molecules based on molecular property values, we introduce a new diversity measure using the Wasserstein distance50 to uniform distributions defined on molecular property values. This property-based measure can play a complementary role to the existing structure-based measures such as the MPD of the Tanimoto coefficients, thus enabling researchers to more profoundly discuss the diversity of molecules. Computational experiments compare the proposed method with the existing structure-based methods and confirm that our method selects more diverse molecules regarding molecular properties. Furthermore, although our method does not explicitly use structure-based descriptors , it successfully selects diverse molecules in terms of MPD values calculated with the Tanimoto coefficient of such structure-based descriptors. We also validate the practical effectiveness of our method via experiments on out-of-domain settings, where we use datasets in different domains between training of GNNs and selection of molecules.This paper proposes a new approach to diverse molecular selection by utilizing the aforementioned GNN-based molecular vectors and the existing submodularity-based selection method. First, we train a GNN with property prediction tasks and use the trained GNN to transform molecular graphs into molecular vectors. Then, we define a submodular function that quantifies the diversity of those molecular vectors as volumes of parallelotopes spanned by them. Owing to the submodularity of the function, we can select near-optimally diverse molecular vectors by using the greedy algorithm. Both GNNs and submodular function maximization are known to be effective in various tasks, and thus each of them has been well studied. However, few existing studies utilize both of them for a single purpose. The only exception is a recent study on multi-robot action selectionThis section presents our molecular selection method, which comprises two steps: training a GNN that generates molecular vectors and selecting GNN-based molecular vectors via submodular function maximization. Figure We also introduce a new property-based diversity criterion, which quantifies the diversity of selected molecules as the Wasserstein distance to uniform distributions defined on molecular property values. Intuitively, we regard a set of molecules as diverse if the property values of those molecules are evenly distributed.41, which is reported to achieve high performances in molecular property prediction. For the sake of completeness, we present mathematical details of GNNs in the “We briefly explain how GNNs generate molecular vectors. GNNs are deep learning architectures that work on graph domains. Taking a graph with node and edge features as input, GNNs capture structures of the graph by iteratively passing messages, which are calculated based on the features. Specifically, each node iteratively receives messages from its neighbors, aggregates them, and pass them to its neighbors; after this message passing phase, a molecular vector, denoted by In the task-specific layer, molecular properties, mentclass2pt{minimGiven molecular vectors generated by the trained GNN, we aim to obtain a set of diverse molecules by selecting diverse molecular vectors. For selecting diverse vectors, we utilize the mathematical framework called submodular function maximization.Submodular function maximization has been studied in the field of combinatorial optimization. This framework enables development of effective diverse selection methods by offering flexible models for representing the diversity and efficient selection algorithms with mathematical performance guarantees; below we detail these two advantages.submodularity, and they are called submodular functions. Many submodular functions have been developed as diversity criteria for various kinds of data such as images, documents, and videos. Therefore, we can choose a suitable function from them for modeling the diversity of molecular vectors. For example, the Shannon entropy is known to satisfy submodularity with respect to the selection of random variables. Other diversity criteria that have submodularity include the ROUGE-N score for document summarization52 and facility location functions53. In the area of bioinformatics, submodular functions for peptide identification are also developed54.The first advantage of using the submodular-function-maximization framework is that there are various known functions for representing the diversity. To find a diverse subset from a large pool of molecules, researchers specify a diversity criterion and search for a diverse subset based on the criterion. Here, a diversity criterion is formally regarded as a set function, which assigns to each subset a real value that indicates how diverse the subset is. Some of such functions have a special property called 44.The second advantage of the submodular-function-maximization framework is that we can utilize various simple, efficient, and mathematically rigorous algorithms for selecting a diverse subset. When selecting a subset from a large number of molecular vectors, there are exponentially many possible candidate subsets. Therefore, we need efficient algorithms for finding diverse subsets. In a series of studies on submodular function maximization, many simple and efficient algorithms for finding subsets with large submodular function values have been developed. Notably, the resulting submodular function values are often guaranteed to be nearly optimal by mathematical analyses. Therefore, once we specify a submodular function as a diversity criterion, we can automatically ensure that those algorithms return highly diverse subsets with respect to the criterion. Among such algorithms, the greedy algorithm is widely used due to its simplicity, efficiency, and strong mathematical guaranteeIn the “log-determinant function, which quantifies the diversity of selected molecular vectors based on the volume of a parallelotope spanned by the selected vectors. As depicted in Fig. 55. Note that the volume-based diversity can capture relationships of vectors that cannot be represented in a pairwise manner. Therefore, the log-determinant function yields a different selection rule than existing methods such as MaxSum and MaxMin, which use pairwise dissimilarities of molecular structures.In our computational experiments, we use a submodular function called a n candidate molecules are numbered by ith molecule is associated with m-dimensional molecular vector n molecular vectors. For any S. We define the log-determinant function m elements are given by Formally, the log-determinant function is defined as follows. Suppose that S while 56. Function Given the function, We refine the GNN-based vector generation process so that it works better with the log-determinant function. Specifically, we make GNNs output non-negative and normalized. Below we detail why we need these refinements and explain how to modify the vector generation process.First, as in Fig. Second, if GNNs are allowed to output vectors with different norms, task-specific layers may distinguish molecules with different properties based on the norm of molecular vectors. In such cases, maximizing the log-determinant function may result in selecting non-diverse vectors due to the following reason. As mentioned above, the log-determinant function represents the volume of the parallelotope spanned by selected vectors, and the volume becomes larger if selected vectors have larger norms. Consequently, molecular vectors with larger norms are more likely to be selected, which may result in selecting molecules with almost the same properties as in Fig. As a result, entclass1pt{minimaBy using our selection method, we can select molecules so that corresponding molecular vectors are diverse. However, even if molecular vectors are diverse, selected molecules themselves may not be diverse. This issue is also the case with the existing structure-based methods, and it has been overlooked in previous studies. That is, the existing methods select molecules that are diverse in terms of the Tanimoto coefficient of molecular descriptors , and thus those methods naturally achieve high mean pairwise distance (MPD) values, which are also calculated by using the Tanimoto coefficient of such descriptors. If we are to evaluate selection methods fairly, we need diversity criteria that do not use molecular descriptors employed by selection methods. This section presents such a criterion for evaluating the diversity of selected molecules in terms of their property values without using molecular vectors. In contrast to the existing structure-based criteria , our criterion is based on the diversity of property values, thus offering a new perspective for evaluating the diversity of molecules.Our idea is to regard molecular property values as diverse if evenly distributed over an interval on the property-value line. We quantify this notion of the diversity using a statistical distance between a distribution of property values of selected molecules and a uniform distribution. As a distance between two distributions, we use the Wasserstein distance, which is defined by the minimum cost of transporting the mass of one distribution to another, as detailed below. We call this diversity criterion the Wasserstein distance to a uniform distribution (WDUD). A smaller WDUD value implies that selected molecules are more diverse since the distribution of their property values is closer to being uniform.V and U be the cumulative distribution functions of the two distributions, respectively. Defining the transportation cost from 50, which we use for quantifying the diversity of property values Formally, WDUD is defined as follows. Let There are other possible choices of statistical distances, such as the variance or the Kullback–Leibler (KL) divergence. However, the Wasserstein distance is more suitable for measuring the diversity than the variance and the KL divergence for the following reasons. If we use the variance of property values of selected molecules as a diversity measure, a set of molecules with extreme property values is regarded as diverse, although this selection is biased since it ignores property values nearby the mean . Compared with our proposed method, this benchmark method is specialized for achieving small WDUD values , while it does not explicitly use information on molecular structures.ith and jth descriptors, we compute the Tanimoto similarity of them and subtract it from 1 to obtain dissimilarity values S whlie We also use MaxMin and MaxSum as baseline methods, which greedily select molecules based on dissimilarities of molecular descriptors. We use MACCS keys and ECFP as descriptors and define the dissimilarity of those descriptors based on the Tanimoto coefficient, i.e., given the n molecules, we compute an MPD value as When evaluating selection methods in the experiments, we also use the mean pairwise dissimilarity (MPD), the existing structure-based criterion, in addition to WDUD. Specifically, given dissimilarity values 58, which is a quantum mechanical dataset with labels of energetic, electronic, and thermodynamic properties computed based on the density functional theory (DFT). Each molecule in the dataset is associated with 12 property values: dipole moment in Debye (mu), isotropic polarizability in 41, we used all the 12 properties to train GNNs. The QM9 dataset contains 133,885 molecules, and we randomly divided them into three datasets as is done in the previous study41: 80%  for training a GNN, 10% (13389 molecules) for validating prediction accuracy of the trained GNN, and 10% (13388 molecules) for a test dataset, from which we selected molecules. Each method selected 133 molecules (1% of the test data) from the test data. Note that when training GNNs, we did not use the test data. We thus created the situation where we select molecules whose property values are unknown in advance.We conducted computational experiments with the QM9 dataset in MoleculeNetThe diversity of property values of selected molecules was evaluated by computing WDUD values for each molecular property. In this evaluation, we used the above 12 properties in the QM9 dataset. However, among the 12 properties, the use of U0, U, H, and G would be inappropriate for evaluating the chemical diversity because their magnitudes depend mostly on the system size. For example, these values are more similar between acetone and acetamide, isoelectronic molecules, than between acetone and methyl–ethyl–ketone, even though most chemists would say that acetone and methyl–ethyl–ketone are both alkyl ketones and chemically more similar. Therefore, we additionally used molecular energy values divided by the number of electrons 60, and the lipophilicity dataset (Lipop)61. ESOL contains 1128 molecules labeled by log-scale water solubility in mol/L. FreeSolv contains 642 molecules labeled by experimentally measured hydration free energy in water in kcal/mol. Lipop contains 4200 molecules labeled by experimentally measured octanol/water distribution coefficient (logD). These property labels were used only when computing WDUD values for evaluation. For each of the three datasets, we selected 100 molecules and evaluated their diversity. Note that unlike the previous case, we select molecules without knowing what properties are used when evaluating WDUD values. Thus, this setting models a situation where we want to select molecules that are diverse regarding some unknown properties.We also conducted computational experiments on the out-of-domain setting. That is, while the GNN is trained with the QM9 dataset, we select molecules from other test datasets than QM9, where we know nothing about the target property labels. This setting is more challenging than the previous one since the test datasets are completely different from QM9; in particular, the target property labels are different from the aforementioned 12 properties of QM9. On the other hand, this setting is more realistic since GNNs are usually trained on some large datasets, while we often want to select molecules from new test datasets that belong to other domains than those of training datasets. In the experiments, we used three test datasets obtained from MoleculeNet: the Delaney dataset (ESOL)SubMo-GNN (Submodularity-based Molecular selection with GNN-based molecular vectors) is our proposed method, which greedily maximizes the log-determinant function55 defined with GNN-based molecular vectors.WG-GNN (Wasserstein Greedy with GNN-based prediction) is our new benchmark method. It selects molecules by greedily minimizing the WDUD values, where molecular property values are predicted by the trained GNN.MS-MK is the existing MaxSum algorithm33 that uses MACCS keys24 as molecular descriptors.MM-MK is the existing MaxMin algorithm35 that uses MACCS keys24 as molecular descriptors.MS-EF is the existing MaxSum algorithm33 that uses ECFP23 as molecular descriptors.MM-EF is the existing MaxMin algorithm35 that uses ECFP23 as molecular descriptors.Random selects molecules randomly according to the distribution of a test dataset.We present the results obtained by the following molecular selection methods:We briefly mention the position of each method. WG-GNN is a benchmark method that is specialized for the diversity of property values, while the structure-based baseline methods, MS-MK, MM-MK, MS-EF, and MM-EF, focus on the diversity of molecular structures. Our SubMo-GNN is an intermediate of the two kinds of methods and can leverage information of both molecular structures and properties, since the GNN-based molecular vectors are generated by taking molecular graphs as input and training the GNN with property-prediction tasks.We evaluated the diversity of property values of selected molecules by the Wasserstein distance to uniform distribution (WDUD). Note that a smaller WDUD value is better since it means the distribution of selected molecules is closer to being uniform.Table In this experiment, each method obtains a single set of molecules, for which we calculate the 18 WDUD values. Therefore, choosing a set of molecules that attains small WDUD values for some properties may result in large WDUD values for other properties. Such a choice of molecules does not meet our purpose, and it is better to balance the trade-off so that none of the 18 WDUD values become too large. A reasonable way to check whether this is achieved is to compare the results with those of Random. If WDUD values of some properties become larger than those of Random, it is probable that selected molecules are biased; that is, the diversity of some properties is sacrificed for achieving small WDUD values of other properties. On the other hand, WG-GNN is expected to achieve almost the best WDUD values since it aims to minimize WDUD values directly . Therefore, we below discuss the results regarding the WDUD values of WG-GNN as benchmarks that are close to the best possible ones.We first discuss the results of SubMo-GNN and the existing structure-based methods in comparison with Random and WG-GNN. Figure 41. This is consistent with our results in Fig. We then compare our SubMo-GNN with the existing structure-based selection methods. Compared to MaxMin-based methods (MM-MK and MM-EF), SubMo-GNN achieved smaller WDUD values for all properties. SubMo-GNN also outperformed MaxSum-based methods (MS-MK and MS-EF) for all but six properties  that encode only structural information of molecules, selected molecules can be non-diverse regarding some molecular properties.We then evaluated selection methods in terms of the diversity of molecular substructures. As a criterion for evaluating the diversity of molecular substructures, we used the mean pairwise dissimilarity (MPD), where molecular descriptors were given by MACCS keys or ECFP. We denote those criteria by MPD-MK and MPD-EF for short. A larger MPD value is better since it implies that selected molecules are more dissimilar to each other. It should be noted that MS-MK and MS-EF greedily maximize MPD-MK and MDP-EF, respectively, and thus they are inherently advantageous in this setting. MM-MK and MM-EF also explicitly maximize the diversity calculated with MACCS keys and ECFP, respectively, and thus this setting is also favorable for them. By contrast, SubMo-GNN and WG-GNN use neither MACCS keys nor ECFP, and thus it has no inherent advantage as opposed to the structure-based methods.Table At this point, the effectiveness of selecting molecules with diverse predicted property values has been confirmed for the case where GNNs are trained on a training QM9 dataset and molecules are selected from a test QM9 dataset, i.e., training and test datasets are in the same domain. In practice, however, we often encounter a situation where GNNs are trained on some large datasets, while we select molecules from new datasets whose domains are different from those of training datasets. In such cases, the diversity of properties registered in the training datasets does not always imply the diversity of molecules in test datasets. Below we experimentally study such out-of-domain settings.We performed experiments on the out-of-domain setting. Specifically, while we trained GNNs on the QM9 dataset as with the previous section, we selected molecules from other test datasets: ESOL, FreeSolv, and Lipop. SubMo-GNN used molecular vectors generated by the trained GNN, and WG-GNN selected molecules greedily to minimize WDUD values of the QM9 properties predicted by using the trained GNN. Note that we cannot train GNNs to predict ESOL, FreeSolv, and Lipop values since nothing about those properties is available. In other words, we consider training GNNs without knowing that they are used for selecting diverse molecules from ESOL, FreeSolv, and Lipop datasets. On the other hand, the structure-based descriptors, ECFP and MACCS keys, have nothing to do with the property labels of test datasets. Therefore, the existing structure-based methods select molecules in the same way as in the previous section. Unlike the previous QM9 case, we selected 100 molecules independently for each of ESOL, FreeSolv, and Lipop since the three datasets consist of different molecules.In this setting, since target property labels and structures of molecules in test datasets are unavailable in advance, we want to select diverse molecules regarding a wide variety of unknown molecular characteristics. To this end, selection methods should not overfit to certain molecular characteristics; they should select molecules that are diverse regarding various aspects, including both property values and structures.Table Table Note that in the above QM9 and out-of-domain experiments, only SubMo-GNN achieved better performances than Random in all criteria. This suggests that the proposed combination of the log-determinant function maximization and the GNN-based descriptors, which are designed to represent both molecular properties and structures, is effective for delivering stable performances in diverse molecular selection regarding various aspects of molecules.The previous experiments confirmed that GNN-based molecular vectors can capture both properties and structures of molecules, which enabled our SubMo-GNN to select diverse molecules. In this additional experiment, we again use the QM9 training and test datasets and present an ablation study to see how the choice of selection methods affects outputs if GNN-based molecular vectors are used as descriptors. Moreover, as an attempt to elucidate how the black-box GNN-based vector generation affects the molecular selection phase, we take a closer look at norms of molecular vectors generated by GNNs and examine how the normalization procedure changes the behavior of selection methods.In this section, all selection methods use GNN-based molecular vectors, and thus we denote our SubMo-GNN simply by SubMo. We use the three selection methods: SubMo, MaxSum (MS), and MaxMin (MM). Each method employs GNN-based molecular vectors with and without normalization, denoted by “w/N” and “w/o N”, respectively, as molecular descriptors. Regarding MaxSum and MaxMin, the pairwise dissimilarity between two vectors is given by their Euclidian distance.Table Figure Since MS greedily maximizes the sum of pairwise dissimilarity values, it prefers selecting molecular vectors that are distant to each other. As a result, MS tend to select molecular vectors with large norms, as we can confirm in the rightmost column of Fig. Compared with MS, SubMo selected molecular vectors with various norms. Therefore, even if norms and property values are strongly correlated, SubMo is expected to select molecules with more diverse property values than MS. As regards normalization, norms of vectors selected by SubMo w/ N were almost the same as those selected by SubMo w/o N, while there is a clear difference between MS w/ N and MS w/o N.To conclude, no single selection method outperforms in all cases, and thus we should employ appropriate selection methods that are suitable for datasets at hand. Nevertheless, MaxSum seems to rely too much on norms of molecular vectors relative to SubMo, and thus we are required to carefully examine molecular vectors when using MaxSum. We finally emphasize that a notable advantage of SubMo is its theoretical guarantee. That is, the log-determinant function values achieved by the greedy algorithm is always at least 63% of optimal function values.We trained Attentive FP with the following hyperparameters: radius = 2, T = 2, fingerprint dimension = 280, dropout = 0.5, weight decay = 0, learning rate = 0.0004, and epoch = 300, where radius and T are the number of times the hidden states are updated in the message passing and readout phases, respectively. In the QM9 experiment, the size of the matrix 56, the other algorithms are implemented in a naive manner and thus have room for acceleration. Therefore, the presented running times are only for reference. Faster implementation of the baseline algorithms is beyond the scope of this paper.We performed computational experiments on Amazon EC2 P3.2xlarge. It has a single Tesla V100 GPU (16GB) and 8 vCPUs (61GB). In the QM9 experiments, training of GNN took 2900 seconds. For selecting molecules, SubMo-GNN, WG-GNN, MS-MK, MM-MK, MS-EF, and MM-EF took 5.1, 5700, 240, 240, 200, and 200 s, respectively. In the ESOL experiments, SubMo-GNN, WG-GNN, MS-MK, MM-MK, MS-EF, and MM-EF took 0.56, 650, 1.7, 1.7, 1.3, and 1.3 s, respectively. In the FreeSolv experiments, they took 0.51, 420, 0.57, 0.57, 0.40, and 0.40 s, respectively, in the same order. In the Lipop experiments, they took 0.58, 2600, 26, 26, 35, and 36 s, respectively, in the same order. Note that while the greedy algorithm in SubMo-GNN used a specialized implementation techniqueWe addressed the problem of selecting diverse molecules for facilitating chemical space exploration. Our method consists of two steps: construction of molecular vectors using the GNN and selection of molecules via maximizing submodular functions defined with molecular vectors. Owing to the use of GNNs trained with property prediction tasks, we can take both molecular structures and properties into account for selecting diverse molecules. Moreover, the submodular function maximization framework enables the greedy algorithm to return subsets of molecules that are mathematically guaranteed to be nearly optimal. We also introduced a new evaluation criterion, the Wasserstein distance to uniform distributions (WDUD), to measure the diversity of sets of molecules based on property values. Computational experiments on the QM9 dataset showed that our method could successfully select diverse molecules as regards property values. Regarding the diversity of molecular structures, it performed comparably to the existing structure-based methods (MaxSum and MaxMin with MACCS keys and ECFP). Experiments with out-of-domain settings demonstrated that our method with the GNN trained on the QM9 dataset could select molecules with diverse property values and structures from out-of-domain datasets: ESOL, FreeSolv, and Lipop. To conclude, our diverse selection method can help researchers efficiently explore the chemical space, which will bring great advances in searching for novel chemical compounds and reactions.We finally mention some future directions. In this study, we evaluated the diversity of molecular properties using the 12 properties of the QM9 dataset, ESOL, FreeSolv, and Lipop. On the other hand, molecular properties used in medicinal chemistry, e.g., Pharmacokinetic properties (logP), drug-likeness (QED), and biological activities, are important in the field of virtual screening. Although the goal of diverse selection is different from that of virtual screening, evaluating diverse selection methods based on properties such as logP and QED may offer an interesting direction of study. Mathematically, studying the relationship between the log-determinant function value and the WDUD value is interesting future work.Supplementary Information.


Nevertheless, mitochondrial respiration is not affected, suggesting specificity for dopamine on mt-HK function. In neural stem cells (NSCs) derived from induced-pluripotent stem cells (iPSCs) of schizophrenia patients, mt-HK is unable to decrease mitochondrial ROS, in contrast with NSCs derived from healthy individuals. Our data point to mitochondrial hexokinase as a novel target of dopaminergic signaling, as well as a redox modulator in human neural progenitor cells, which may be relevant to the pathophysiology of neurodevelopmental disorders such as schizophrenia.Dopamine signaling has numerous roles during brain development. In addition, alterations in dopamine signaling may be also involved in the pathophysiology of psychiatric disorders. Neurodevelopment is modulated in multiple steps by reactive oxygen species (ROS), byproducts of oxidative metabolism that are signaling factors involved in proliferation, differentiation, and migration. Hexokinase (HK), when associated with the mitochondria (mt-HK), is a potent modulator of the generation of mitochondrial ROS in the brain. In the present study, we investigated whether dopamine could affect both the activity and redox function of mt-HK in human neural progenitor cells (NPCs). We found that dopamine signaling via D Drosophila melanogaster increased visual responsiveness and locomotion in adult animals and reduced gamma-like activity, resembling the symptoms observed in patients with schizophrenia . C. C1R) ann of G6P C. In addn of G6P C,D. The lopride) C,D, demoMitochondria play an essential role in calcium homeostasis through the uptake of cytosolic calcium. Recently, de-Souza-Ferreira (2019) demonstrated that mt-HK plays an important role in calcium uptake by brain mitochondria because mt-HK inhibition by G6P impairs mitochondrial uptake of calcium, whereas mt-HK activation by 2-DOG improves the uptake . TreatmeOverall, these data demonstrated that dopaminergic signaling in human neural progenitor cells affects the redox function of mitochondria-bound hexokinase to eventually impair normal modulation of mitochondrial calcium handling.Dysregulation of dopamine signaling was proposed to be involved in the pathophysiology of psychiatric disorders, such as schizophrenia ,44; henc1R-dependent signaling in human NPCs. This effect resulted in an increase in extracellular H2O2 and impairment of mitochondrial calcium dynamics in dopamine-treated NPCs.The present study showed that dopamine impairs the modulation of mitochondrial ROS by decreasing mt-HK activity via DInitially, we determined whether dopamine treatment influences ROS production in human NPCs since ROS are naturally produced during enzymatic metabolism of dopamine or nonenzymatic autoxidation of dopamine degradation products . Surpris2O2 in response to dopamine treatment of human NPCs was significantly higher  or D2R (raclopride) were used to investigate the mechanism by which dopamine affects mt-HK in human NPCs. We demonstrated that NPCs used in the present study expressed both dopamine receptors  between NSCs derived from healthy and schizophrenic subjects. Other studies used patient-derived cells and reported impaired mitochondrial respiration in iPSCs  and duri2R. However, D2R signaling alterations are associated with schizophrenia symptomatology in adults, as medication used for controlling positive symptoms are mostly D2R antagonists [1R activity was demonstrated to increase the formation of cerebral organoid by human NSCs through modulation of proliferation and differentiation [2R [Most data on dopaminergic alterations in schizophrenia involve signaling via Dagonists ,65. In tagonists  that invntiation . On the tion [2R .1R-mediated dopaminergic signalling, which impairs the role of mt-HK as a redox modulator in NPCs. Similarly, impaired control of ROS production by mt-HK was shown in NSCs derived from schizophrenia patients; these findings may indicate a correlation between dysregulation of dopaminergic signalling during neurodevelopment with pathophysiology of schizophrenia. Applicability of these findings to other cell types in addition to neural progenitor cells used in the present study remains to be determined. The results of the present study obtained using various approaches suggest that redox balance fine-tuned by mt-HK may be disrupted by dopamine signalling in human brain cells.Overall, our data highlight the role of mitochondrial hexokinase, a potent redox modulator in mature neurons, as an equally relevant redox modulator in human neural progenitor cells. Importantly, we propose that mt-HK is a novel target of DClick here for additional data file.


Molecular characterization of ependymoma has revolutionized its categorization. This new molecular classification has implications particularly in targeted therapeutics. Amongst the ten subgroups of ependymoma currently described, three are found in the spinal compartment, and three in the infratentorial and supratentorial compartments respectively; the subependymoma subgroup is found in all these anatomic compartments. Each subgroup carries unique molecular features that lead to oncogenesis and to disparities in prognosis. Here, the molecular classification, key clinical features, current understanding of tumorigenesis, and potential molecular targets for cranial and spinal ependymoma are discussed.Ependymoma is a biologically diverse tumor wherein molecular classification has superseded traditional histological grading based on its superior ability to characterize behavior, prognosis, and possible targeted therapies. The current, updated molecular classification of ependymoma consists of ten distinct subgroups spread evenly among the spinal, infratentorial, and supratentorial compartments, each with its own distinct clinical and molecular characteristics. In this review, the history, histopathology, standard of care, prognosis, oncogenic drivers, and hypothesized molecular targets for all subgroups of ependymoma are explored. This review emphasizes that despite the varied behavior of the ependymoma subgroups, it remains clear that research must be performed to further elucidate molecular targets for these tumors. Although not all ependymoma subgroups are oncologically aggressive, development of targeted therapies is essential, particularly for cases where surgical resection is not an option without causing significant morbidity. The development of molecular therapies must rely on building upon our current understanding of ependymoma oncogenesis, as well as cultivating transfer of knowledge based on malignancies with similar genomic alterations. Ependymomas are primary central nervous system (CNS) tumors derived from the ependymal lining of the ventricular system ,2. TheseBased on the Central Brain Tumor Registry of the United States, ependymal tumors represent approximately 1.6% of all primary CNS tumors. Incidence of ependymal tumors ranges from 0.29 to 0.6 per 100,000 person-years and is lowest in the first two decades of life and highest in the 65–74 years old age group . Tumor lManagement of adult patients with ependymoma involves surgical resection for its cytoreductive effects and, in many cases, to restore normal cerebrospinal fluid (CSF) dynamics ,7,8. PosThe management of pediatric patients with ependymoma follows a similar paradigm except for several key differences. Pediatric ependymoma management centers around GTR and radiotherapy. Based on National Cancer Institute recommendations, all intracranial pediatric ependymoma cases require surgery and postoperative radiotherapy unless the patient is under 1 year of age . The EurThe management strategy of adult patients that suffer from relapse is currently evolving as our understanding of the molecular drivers of this disease are being further investigated . MoreoveRELA-fusion ependymoma, to enhance the diagnostic and prognostic utility of this classification strategy [Ependymomas have classically been diagnosed based on the WHO histological classification strategy, which was designed to integrate histopathological characteristics to facilitate tumor diagnosis, guide treatment strategy, allow for prognostication, and aid research by allowing for interlaboratory comparisons. In 2016, the WHO classification for CNS tumors incorporated not only light microscopy, immunohistochemical lineage-associated proteins, and ultrastructural characteristics, but also a few molecular markers, namely strategy ,34,35,36The introduction of molecular markers in the 2016 WHO classification and the expansion in the 2021 WHO classification, was a function of several investigations elucidating the genetic uniqueness within the histologically defined subgroups of ependymomas. These findings began as a series of multi-omics studies that separated genomic groups of ependymomas with clinical and prognostic correlation. Early studies focused on gene expression and copy number profiles, while late studies focused on epigenomics ,42,43,44YAP1 fusion (ST-YAP1), and ST ependymoma with RELA fusion (ST-RELA). All nine subgroups were found to have distinct DNA methylation patterns that correlated with pertinent clinicopathological variables, such as age and sex. The study was also able to demonstrate that risk stratification by molecular subgrouping is superior to the histopathological grading used by the 2007 WHO grading system. However, it is important to note that in this and following studies, many classic histology ependymomas (grade II) were classified into the SE and MPE subgroups [In 2015, a landmark clinical and genomic study by Pajtler et al., utilized DNA methylation profiling of 500 ependymomas to identify nine subgroups . This prubgroups ,40. NeveC11orf95/ZFTA-fusion subgroup (same as the former 2016 RELA-fusion subgroup), YAP1-fusion subgroup, PFA subgroup, PFB subgroup, and the SP ependymoma with MYCN amplification subgroup. These were added as unique genomic markers, and transcriptional patterns were identified that could better differentiate ependymomas despite similar histological features [In 2021, the WHO Classification of Tumors of the CNS will be again updated with its 5th edition attempting to capture all of the recent updates in genomic profiling and to address some of the aforementioned issues. This update introduced the features ,48,49,50The 2021 WHO classification, the Pajtler study, and the subsequent studies that examined these distinct subgroups in the population, their underlying oncogenic drivers, as well as proposed molecular targets, are the subject of this review. Besides, SE and MPE which remain their own histological categories, ependymomas are now primarily defined by anatomic location , histology (grade 2 or 3), and molecular features ,50. In tIn the 2021 CNS WHO classification, subependymoma (SE) remains a separate entity  identified by morphologic criteria A as therSpinal subependymomas (SP-SE) have a predilection for the cervical or cervicothoracic junction, and typically present with insidious myelopathy or radicular pain . This suPatients with SP-SE have an excellent prognosis when treated with the standard of care treatment strategy of microsurgical resection . GTR witc-MYB), a proto-oncogene encoding a transcription regulator that plays an essential role in regulation of hematopoiesis and located on chromosome 6q23.3, is being explored as a driver in the oncogenesis of this tumor [EPHA7, a tumor suppressor gene found on 6q that encodes the protein Ephrin type-A receptor 7, a receptor tyrosine kinase that mediates developmental events in the nervous system [EPHA7 may contribute in part to the onset of T-cell lymphomas.Research into the molecular drivers underlying oncogenesis of SP-SE has been sparse, and likely secondary to its benign clinical course. The two largest genetic analyses of this tumor subgroup both showed loss of 6q ,40. In ais tumor . Additios system . EPHA7 mThe histopathologic origin of SE in the IT compartment remains uncertain. Possible cell origins include ependymal-glial precursor cells, subependymal plate cells, and tanycytes ,63. MacrPF-SE carries low pathogenicity and, in fact, is often not discovered until postmortem examination ,65. GTR TOP2B and is a key decatenating enzyme [HIF1A and functions as a master transcriptional regulator in the response to hypoxia. E3 ubiquitin-protein ligase Mdm2 is encoded by MDM2 and functions to mediate ubiquitination of p53 leading to its degradation. Nucleolin is encoded by NCL and is a major nucleolar protein associated with intranucleolar chromatin and preribosomal particles that induces chromatin decondensation by binding to histone H1. Given the benign course of PF-SE, there are no chemotherapy regimens commonly used, nor are there any current targeted therapies for this disease. At the time of writing, there are no clinical trials for chemotherapies or molecularly targeted therapies for SE. KIT may be a viable target as it is only expressed at high levels in PF-SE tumors but not the other subgroups [Genomic analyses of PF-SE have had mixed results regarding copy number variations ,40. Whilg enzyme . HIF-1α ubgroups ,71. Topoubgroups .Supratentorial subependymoma (ST-SE) is often attached to the wall of the ventricular system. While the IVthV is a common intracranial location , SE is most commonly found supratentorially in the lateral ventricles , as subependymal glial cells are thought to play a large role in the origin of this neoplasm ,73,74. SThe current gold standard treatment is maximal resection ,76. The Because of the uncommon nature of this subgroup, as well as the excellent prognosis following GTR, there has been little research regarding molecular targets for ST-SE. Similar to SP-SE and PF-SE, the two largest genomic studies have shown disputing results regarding copy number variations ,40. The Myxopapillary ependymoma (MPE) is a tumor originating almost exclusively in the spine . MPE was previously classified as WHO grade I in the 2016 WHO CNS classification, but because there is evidence that its outcomes are comparable to those of classic ependymoma, the new 2021 WHO CNS classification recommends assigning grade 2 to MPE ,49,50. MMPE is retained as a separate entity in WHO 2021 and defined by histological criteria. Methylation analysis by Pajtler et al., separated a MPE methylation subgroup from two SE and six classic ependymoma methylation subgroups ; howeverSimilar to SP-SE, the gold standard treatment modality for MPE is maximal safe resection . A systehomeobox B13 (HOXB13), a gene encoding a transcription factor that regulates skin development, has been shown by multiple studies [HOXB13, homeobox A13 (HOXA13), homeobox C10 (HOXC10), and homeobox D10 (HOXD10), all of which are genes overexpressed in the developing lumbar spine [neurofilament light chain (NEFL), encoding a Class IV intermediate neurofilament expressed in neurons and located on chromosome 8p21.2 in close proximity to transcription factor binding sites of HOX genes, and platelet derived growth factor receptor alpha (PDGFRA), a gene which encodes a tyrosine kinase [HOXB13, lending further evidence towards the importance of this gene [cyclooxygenase-2 (COX2), a gene that encodes for cyclooxygenase, which plays a vital role in inflammation and has been shown to be overexpressed in various cancers as it also plays a role in cell proliferation, neovascularization, and tumor metastasis [The current state of understanding of the oncogenic drivers that lead to MPE is still in its infancy as the excellent prognosis with surgery has not necessitated targeted therapies. While chromosomal instability is considered to be the defining genomic characteristic of this subgroup of ependymoma, as seen by the copy-number variation gains observed by Pajtler et al. in their DNA methylation array of 26 samples of MPE, there are three notable genes that are areas of current research and possible future molecular targets . Overexp studies ,89. The ar spine . Immunohar spine . Two othe kinase . While this gene . This sttastasis ,94,95,96neurofibromatosis type 2 (NF2) mutations or deletions as seen in the molecular study by Pajtler et al., which found 90.5% of samples categorized into the SP ependymoma group demonstrated copy number variations of chromosome 22q where this gene is located [Spinal ependymoma (SPE) (WHO grade 2 or 3) is a tan, soft, well-circumscribed tumor composed of monomorphic glial cells with round to oval nuclei with speckled chromatin. They form perivascular pseudorosettes and/or true ependymal rosettes. When hypercellularity, cellular atypia, frequent mitoses, abundant endothelial proliferation and/or palisading necrosis are present, the tumor is deemed to be WHO grade 3. Histological patterns  can be seen but do not have clinical significance ,49,50,97 located .Unlike MPE and SE, which are easily distinguishable entities, the 2016 WHO classification of ependymoma into grades II or III is less consistent . StudiesThe standard treatment modality is microsurgical resection with the goal of GTR with minimal normal tissue disruption; however, the role of adjuvant radiotherapy is a topic of debate. One series of 104 (101 grade II and 3 grade III) SPEs that underwent surgical resection found the median PFS of those that underwent microsurgical resection to be 14.9 years for grade II and 3.7 years for grade III SPEs. Furthermore, they reported no significant change in PFS for patients that underwent adjuvant radiotherapy following STR . A more NF2 alterations in other CNS tumors has led to extensive research being performed to better understand the function and oncogenesis that occurs in tumors harboring this genomic variation. NF2 is a tumor suppressor gene, located on chromosome 22q12.2 and codes for the protein Merlin. Deletions or loss-of-function mutations of this gene leads to neurofibromatosis type II, which is inherited in an autosomal dominant pattern [Despite this excellent prognostic profile, the presence of  pattern . Merlin  pattern . Second, pattern . In this pattern .NF2-associated tumors ). A phase 2 clinical trial is also looking at NF2 specific molecular targets via a FAK inhibitor on NF2 mutant meningiomas (NCT02523014/A071401). Another NF2 specific molecular pathway target is verteporfin a benzoporphyrin derivative that is currently used as a photosensitizer in macular degeneration, as it has been shown to disrupt YAP oncogenic activity [NF2-associated schwannomas, meningiomas, and ependymomas [As robust research has allowed for the meticulous understanding of the role of Merlin in oncogenesis, there have been several treatments under investigation targeting these molecular pathways. Small molecular MEK inhibitors are currently under investigation as they seek to target and inhibit the RAF/MEK/ERK pathway for activity ,111. Othndymomas ,112. Lasndymomas ,114,115.MYCN amplification (SPE-MYCN) has recently been added as a new molecular category of ependymoma based on the updated 2021 CNS WHO classification, which was based upon several studies that have identified the presence of this molecular signature in a subgroup of highly aggressive SPEs. In 2001, Scheil et al. was the first to report on the amplification of Myelocytomatosis-N (MYCN), a gene located on chromosome 2p24.3 encoding a proto-oncogene transcription factor located in the cell nucleus that is critical for normal CNS development. This finding was based on a comparative genomic hybridization study of 26 ependymomas in 22 patients. Two cases of SPE-MYCN were identified with histological characterizations leading to a diagnosis of SP ependymoma WHO Grade III, one of which had relapse and intracranial metastases [MYCN amplification in a cohort of 13 tumors, of which 10 were WHO Grade III and three were WHO Grade II [MYCN amplification in all recurrent tumors assessed, as well as the presence of MYCN protein overexpression by immunohistochemistry, suggesting malignant progression being driven by increased MYCN gene expression. Using a similar DNA methylation assay, two other studies recorded similar results, leading to a total of 27 published cases of ependymoma with MYCN amplification, which has led to the categorization of this subset of SPE [MYCN, a member of the family of MYC oncogenes, plays a large role in neurogenesis and has been implicated in the genesis of various CNS malignancies such as neuroblastoma, pediatric glioblastoma, and medulloblastoma, as well as malignancies outside of the CNS such as leukemia and prostate cancer [Spinal ependymoma with tastases . Later, Grade II . This set of SPE ,119. Alte cancer ,123,124.e cancer .Posterior fossa ependymoma (PFE) has been studied and classified by many studies. Initially these studies used gene expression and copy number profiles, and defined two groups of tumors: Groups 1 and 2 by Wani et al., and Groups A and B by Witt et al. and by Hoffman et al. ,43,44. Tn = 17 in this study) were classified as SE [Although the molecular characterization of PFEs has caused significant change in the categorization of these tumors, the DNA methylation results should be interpreted with care and in context with histology. There are many reasons why bioinformatics analyses (of any kind) should be interpreted with care, but this is beyond the scope of this article. Briefly, this starts with the quality of the tissue processed , the accuracy of tissue selected (tumor vs something else) and the algorithms used for data normalization. Similarly, the parameters, cut-off scores, and functions used for the analysis proper are of outmost importance. In the case of methylation there is no gold standard as of yet, and although the bioinformatic strategy used in the largest study published so far on brain tumors is now being commercialized, this algorithm was not sufficiently validated. Moreover, although this study included a large number of samples overall, when divided per rare entities the number of samples was small . Therefoed as SE . Similared as SE .PFEs are histologically grade 2 or 3, as described above in the SPE section D–F. As tPFA is primarily a pediatric disease, with a median age of 3 years, and age ranging from 6 months to 58 years old across studies ,42,127. In regard to ependymal tumors of the IT region, PFA has the poorest prognosis. Additionally, its prognosis is among the most dismal of all ependymoma subgroups. In comparing patients older than 10 years to those younger, the 5-year PFS and OS was 54% and 71%, which was not significantly different than patients younger than 10 years ,40. AlthH3K27M mutations and, in very rare instances (0.6–4.2% of cases), PFAs share this mutation [H3K27M mutations induce derepression of pro-oncogenic transcription factors through global reduction of histone 3 K27 trimethylation, H3K27me3. In addition to sharing this global DNA hypomethylation, PFA tumors have increased H3K27me3 enrichment at select genomic loci similar to that of H3K27M-mutant DMG [Overall, PFA tumors appear to have balanced genomes with the most frequent copy number variations being 1q gains (17.3–25%) and 6q losses (6.4–8.6%), both of which are negative prognostic indicators ,135,136.mutation ,138. In tant DMG .cis-acting gene regulatory elements [H3K27M mutations [An important oncogenic driver in PFA is Cxorf67, or EZH Inhibitory Protein (EZHIP), and its interaction with polycomb repressive complex 2 (PRC2), a histone methyltransferase that primarily methylates H3K27. Both Histone H3K27M and Cxorf67/EZHIP have short sequences that bind and inhibit PRC2 . These selements . Importautations . The lacutations ,140,142.utations ,130. Alputations .hTERT promoter and chromosome 1q gain were both strongly associated with telomerase reactivation in PFA.There is evidence that the microenvironment plays a strong role in PFA growth and propagation ,145,146.H3K27M-mutant DMG, medications directed at this cancer may also have therapeutic value in PFA [H3K27M-mutant DMG, revealed the combination multihistone deacetylase inhibitor panobinostat and the proteasome inhibitor marizomib to have the highest therapeutic value and thus may have applicability in the PFA setting [Currently, there are no established targeted therapies for PFA. As PFA tumorigenesis appears similar to e in PFA . Quantit setting . Given t setting . In a pi setting . Lastly, setting ,140. Int setting . A recen setting .Posterior fossa ependymoma Group B (PFB) tumors occur in adolescent and adult populations, with a median age of approximately 30 years and an age range of 2 to 65 years across studies ,42,127. FOXJ1) [FOXJ1 encodes Forkhead box protein J1, which is an important regulator of motile ciliogenesis, has been associated with Shh signaling, and is highly expressed in ependyma and choroid plexus [In comparison to the balanced chromosomal composition of PFA, PFB is characterized by a high degree of chromosomal instability, with many copy number aberrations present ; most noFOXJ1) ,44,152. d plexus ,154. Thed plexus . Howeverd plexus ,157. In d plexus ,159. Intd plexus . There ad plexus ,161,162.d plexus . AlthougYAP1-fusions (ST-YAP1) and those that have ZFTA-fusions (ST-ZFTA) [The new 2021 WHO Classification of CNS tumors included two main subsets of ependymomas located in the ST space: those that have ST-ZFTA) ,50. The ZFTA-fusion, with most reports of 5-year OS at 100% [YAP1 gene with chromosome X MAMLD1 gene, though fusion with another gene, FAM 118B [YAP1 gene, is a downstream effector in the Hippo signaling pathway [TAZ) in a similar but non-redundant fashion to complex with TEAD to act on gene targets [PD-L1) gene expression, thus attenuating T-cell activation [Supratentorial ependymoma with YAP1-fusion (ST-YAP1) is primarily a pediatric subgroup of ependymoma, with the median age of approximately 1 year and an age range from seven months to 51 years across studies ,164,165. at 100% ,165. TheFAM 118B ,165. YAP pathway . YAP int pathway . YAP wor targets . Prevent targets . In anot targets . This su targets ,170. YAP targets . In a pr targets . YAP hastivation ,172.In the setting of breast cancer cell cultures that depend on YAP/TAZ, the use of on-market drugs dasatinib, fluvastatin, and pazopanib inhibit nuclear location of YAP/TAZ . DasatinST-ZFTA is the second main subgroup of ependymoma in the ST compartment and harbors the worst outcome . ST-ZFTAZFTA fusion but is neither sensitive nor specific, while p65/double immunostaining has a 92% positive predictive value and a 100% negative predictive value [REL-associated protein (RELA) fusing with the gene Zinc Finger Translocation Associated (ZFTA), also known as C11orf95, being thought to be the distinguishing molecular characteristic. The reason for this change in nosology is based on several studies showing that both RELA and ZFTA fuse with various genes secondary to chromothripsis. However, ST-ZFTA demonstrates a consistent histomolecular entity following ZFTA fusion with or without RELA, thus underscoring the molecular importance of the presence of ZFTA fusion, as opposed to RELA [ZFTA fusion, and Pajtler et al. observed the presence of this genomic fusion in 72% of ST ependymomas [ZFTA, the main fusion partners that have been identified and shown to be sufficient for tumorigenesis are Mastermind Like Transcription Coactivator 2 (MAML2), Nuclear Receptor Coactivator 2 (NCOA2), and Nuclear Receptor Coactivator 1 (NCOA1) [On immunohistochemistry, L1 Cell Adhesion Molecule (L1CAM)/(CD171) corresponds to the presence of ve value . ST-ZFTA to RELA ,183,184.ndymomas ,181. Whi (NCOA1) .ZFTA-fusion subgroup was, in large part, a result of the landmark study by Parker et al. that identified the fusion of RELA with ZFTA in two thirds of ST ependymoma [ZFTA. While the fusion of ZFTA has been established as the molecular marker distinguishing this subgroup, the function and role of ZFTA is still not completely understood. However, the introduction of this molecular subgroup has bolstered investigation into the function of ZFTA, and a recent study by Kupp et al. utilized a combination of transcriptomics, chromatin immunoprecipitation sequencing, and proteomics to elucidate the mechanism behind this gene [ZFTA is only recently being unraveled, RELA, the principal effector of canonical Nuclear factor-κB (NF-κB), has been well studied, and its tumorigenesis mapped out. RELA is normally located in the cytoplasm via nuclear factor of kappa light polypeptide gene enhancer alpha (IkBα)-mediated sequestration; however, upon external pressures, RELA protein is translocated to the nucleus to influence the NF-κB pathway [PLAGL (PLAG1 Like Zinc Finger 1)-enriched sites across the genome [Fibroblast Growth Factor Receptor 3 (FGFR3). FGFR3, a gene located on chromosome 4 and responsible for bone development, has been shown to be overexpressed in an in-vitro model of ST-ZFTA. This study also demonstrated the efficacy of a broad range of FGFR inhibitors in inducing maturation in their invitro model, a therapeutic concept currently considered of high potential in pediatric cancers [cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) may play a role, as it influences the TP53 pathway which has been linked to chromothripsis in other tumors [The introduction of the endymoma . These this gene . This wo pathway ,188. Par pathway . Therefo pathway ,190. Adde genome . Moreove cancers . Lastly, cancers . T-cell  cancers . This obr tumors ,195. GivThe novel description of the molecular origin of ependymoma has led to further understanding of this genetically diverse tumor. When compared to histopathological classification, the molecular categorization of ependymal tumors into 10 distinct subgroups has led to a more refined characterization of clinical course and potential molecular targets.clinicaltrials.gov” (accessed on 9 November 2021). As the molecular underpinnings of ependymomas are further unraveled through the groundbreaking work of investigators in the field, it is expected that this list will continue to grow.Given the unique profile of each ependymoma subgroup, future research must rely on molecular characterization rather than classical histopathological categorization alone. Genomic profiling of ependymal tumors is essential in the further elucidation of molecular targets. While many of ependymoma subgroups are aggressive, even the benign subgroups urgently need targeted therapies, as many tumors are not amenable to surgical resection without causing substantial disability. Research must continue to build on studies that have elucidated ependymoma oncogenesis, as well as investigate novel targets based on malignancies with similar gene ontology. An up-to-date clinical trial list regarding targeted treatments for ependymoma is available on “


Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments to compare with lab-maintained ecosystems.Many ant species grow fungus gardens that predigest food as an essential step of the ants’ nutrient uptake. These symbiotic fungus gardens have long been studied and feature a gradient of increasing substrate degradation from top to bottom. To further facilitate the study of fungus gardens and enable the understanding of the predigestion process in more detail than currently known, we applied recent mass spectrometry-based approaches and generated a three-dimensional (3D) molecular map of an IMPORTANCE The study of complex ecosystems requires an understanding of the chemical processes involving molecules from several sources. Some of the molecules present in fungus-growing ants’ symbiotic system originate from plants. To facilitate the study of fungus gardens from a chemical perspective, we provide a molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments. Many ant species access plant-derived nutrients with the help of fungal symbionts . Ant fun9–13–16–19–24–Atta texana fungus garden using molecular networking -9-oxo-11-(3-pentyloxiran-2-yl)undec-10-enoic acid (compound 3)  and C30H45O3  and belong to the same molecular family (A colony of e garden . Our metpound 3) , phytosppound 3) , and othpound 3)  to S9. Td 3)  to , decreasd 3)  to . These gd 3)  to  to S9. W  to  to  and S7,   to  to , whereas  to  to , 41,  Molecular network highlighting molecular families discussed in the manuscript containing putatively identified compounds from GNPS spectral libraries. Connected nodes correspond to structurally related molecules based on their spectral similarity. Color codes as described in the figure. Closer looks are provided in in silico tools using the MolNetEnhancer workflow available in the GNPS platform. Color codes as described in the figure represent the most predominant putative annotation of the detected features per molecular family at the superclass chemical taxonomy level. Additionally, the molecular families containing putatively identified compounds from GNPS spectral libraries are highlighted and labeled. Download FIG S1, TIF file, 1.6 MB.Molecular network from deconstructed Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00601-21.2FIG S2Atta texana fungus garden. Separation of layers of the fungus garden is clearly observed between the top layer (A layer) against the other fungus layers . From the top, 10 features with a high loading score (VIP score) from PLS-DA are shown with annotation at the subclass level based on candidates from in silico approaches when no identifications were provided by GNPS libraries; otherwise, the library match is specified at the top. The whisker plots for the detected features correspond to normalized values of abundance (y axes) using an internal standard (sulfamethazine) as reference feature. Samples, as shown in the x axes correspond to the following labels: maple leaves (L), layers of the fungus garden from top to bottom , and trash from top to bottom . Download FIG S2, TIF file, 1.4 MB.Chemical features with high contribution in differentiation of samples from Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00601-21.4FIG S4A. texana fungus garden. In this figure, the detected feature corresponding to kaempferol  is highly abundant in the plant material and in the top layer of the fungus garden compared to that of the remaining layers of fungus garden and trash material. Connected nodes correspond to structurally related molecules based on their spectral similarity and due to their distribution (mainly fungus garden); they might be products of biotransformation processes occurring in the fungus garden. The annotation of kaempferol from GNPS libraries (cosine 0.93) was confirmed by using a reference standard . Download FIG S4, TIF file, 1.8 MB.Molecular cartography of kaempferol detected in the Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00601-21.6FIG S6A. texana fungus garden. In this figure, the detected feature corresponding to ginnalin A, the node inside the red circle, corresponds to compound 2 in the main text (m/z 469.0971) and is highly abundant in the plant material as well as in the top layer of the fungus garden. The identification of ginnalin A based on spectral similarity to GNPS libraries (cosine 0.95) corresponds to annotation at level 2 . Download FIG S6, TIF file, 1.8 MB.Molecular cartography of ginnalin A detected in the Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00601-21.7FIG S7A. texana fungus garden. (a) (Left) Molecular family of phytosphingosine-related molecules. (Right) Spatial distribution of phytosphingosine , the node inside the red circle, corresponds to compound 4 in the main text. (b) (Left) Zoomed-in molecular family of sterol-related molecules. (Right) Spatial distribution of ergosterol peroxide , the node inside the red circle, corresponds to compound 1 in the main text. The annotation of phytosphingosine from GNPS libraries (cosine 0.72) and ergosterol peroxide (cosine 0.71) were confirmed by using reference standards. Download FIG S7, EPS file, 1 MB.Molecular cartography of phytosphingosine and ergosterol peroxide detected in the Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00601-21.8FIG S8E)-9-oxo-11-(3-pentyloxiran-2-yl)undec-10-enoic acid and putative oxylipin derivative detected in the A. texana fungus garden. (a) (Left) Zoomed-in molecular family of oxylipin-related molecules. (Right) Spatial distribution of (E)-9-oxo-11-(3-pentyloxiran-2-yl)undec-10-enoic acid, also known as trans-EKODE-(E)-Ib . The node inside the red circle corresponds to compound 3 in the main text. The annotation of compound 3 from GNPS libraries (cosine 0.86) was confirmed by using a reference standard. (b) (Left) Molecular family of oxylipin-related molecules. (Right) spatial distribution of an unknown oxylipin member of this molecular family . The arrow indicates that higher abundance of this feature was observed at the top of the trash material and considerably decreased to low abundance at the bottom of the trash material. It is possible the abundance is associated with accumulated biomass, higher at the top, although this needs further investigation. This identification corresponds to a level 3 annotation (molecular family). Download FIG S8, TIF file, 1 MB.Molecular cartography of  (Left) Zoomed-in molecular family of pentacyclic triterpenoid-related molecules. The chemical structure of 11,12-dehydroursolic acid lactone  corresponding to C30H46O3  is provided to illustrate this pentacyclic triterpenoid molecular family. (Right) Spatial distribution of a member of this molecular family  consistent with the molecular formula C32H50O2. Although the distribution of this feature belonging to triterpenoid molecular family seems vague, it is in high abundance in the plant material and at the top layer of the fungus garden. This distribution contrasts with the distribution of the feature m/z 423.3609 (b) and suggests this chemical transformation corresponding to the mass shift of 44.026 Da might occur in the fungal garden accumulating the product (feature m/z 423.3609) with high abundance at the bottom layers from the fungus garden and trash material. (b) (Left) Zoomed-in molecular family of pentacyclic triterpenoid-related molecules. (Right) Spatial distribution of a member of this molecular family  consistent with the molecular formula C30H46O. This figure shows the distribution of this molecular feature with high abundance at the bottom layers from the fungus garden and trash material. Additionally, the abundances are higher in the trash material than in the fungus garden. Similarly, as observed for steroid and oxylipins, it is possible that metabolic transformations of terpenoids might be mediated by microorganisms present in the trash material. The annotation of the molecules shown at the molecular family level corresponds to a level 3. Download FIG S9, TIF file, 1 MB.Molecular cartography of triterpenoid derivatives detected in the Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2), and their distribution between two locations , we can discover related molecules that have the largest differential abundance between the compared locations   . It shouMethods) . We inte10.1128/mSystems.00601-21.10FIG S10m/z 311.2206 and m/z 474.3783 shows a mass shift of 163.158 Da, putatively corresponding to C8H21NO2. The annotation of the feature corresponding to m/z 311.2206 (E)-9-oxo-11-(3-pentyloxiran-2-yl)undec-10-enoic acid (compound 3 in the main text) from GNPS libraries (cosine 0.86) was confirmed by using a reference standard. The feature corresponding to node m/z 474.3783  was associated only to trash material, while node m/z 311.2206 was detected in the bottom layer of the fungus garden and at low abundance in the trash material. The boxes represent the 25%, 50%, and 75% quartiles, and the whiskers extend ±1.5 times the interquartile range; the jitters represent samples. (b) A molecular family of acylglycerol compounds (left) are highlighted from a molecular network based on their high proportionality score. The feature corresponding to m/z 515.3209  was associated only to plant material, while the feature m/z 677.3751, compound 9 in the main text , was detected across the samples, including fungus garden and trash material. The mass difference from this network pair was 162.053 Da, corresponding to a molecular formula C6H10O5, which is consistent with a sugar moiety. This example indicates that these compounds are involved in chemical transformation (gain of 162.053 Da) that occurs mainly in the fungus garden. For the structures shown, it was not possible to isolate sufficient quantities to analyze by nuclear magnetic resonance (NMR); therefore, MS/MS matching to reference spectra was the only strategy to gain insight into the structural modifications. The identification of monolinolenin based on spectral similarity to GNPS libraries (cosine 0.75) corresponds to annotation at level 2, while the match of the related molecules shown are at the molecular family level, a level 3 annotation. For the monolinolenin derivatives, although chain length and number of double bonds are known, the location of the double bonds, cis or trans configuration of the double bonds, or stereochemistry of the saccharide are not. Download FIG S10, TIF file, 1.1 MB.Chemical proportionality of oxylipins and acylglycerol derivatives molecular family. (a) Chemical features are highlighted from a molecular network based on their high proportionality score calculated between the bottom layer of the fungus garden and top layer of the trash material. The network pair Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the A. texana fungus garden, and the associated mass shifts provided evidence of the types of modifications occurring in the sampled locations. An example of this proportionality approach presented in m/z 469.0971 , annotated as the bioactive phenolic compound ginnalin A (compound 2)  and m/z 467.0818  were only detected in the plant material, possibly indicating both molecules are transformed in the fungus garden to either compound 2 or a related molecule, such as features m/z 315.0717  and m/z 771.1034 . Modification of phenolic compounds in fungus gardens has been described as a mechanism of detoxifying these common plant defenses against defoliating herbivores  , to be irbivores , 50, 51.rbivores , suggest2 (2.015 Da) were the predominant type of chemical transformation observed throughout the data set, being one of the most frequent mass shifts with a proportionality score of >1 among leaves, the fungus garden, and trash layers (2 (14.015 Da) and C2H4 (28.031 Da) were other common changes observed in the top layer of the fungus garden and between the bottom layer of the fungus garden and the trash , were associated with plant material and the top layers of the fungus garden (O-pentoside (compound 10) and quercetin (compound 11).Proportionality scoring is a logarithmic expression, and we selected an absolute value of 1 as prioritization cutoff, because a score close to zero indicates a low ratio of differing abundance between two chemically related molecules in two sample locations resulting from a small change in abundances between the two features . Differences representing a gain or loss of Hh layers . This coh layers  and phyth layers . Mass shhe trash . Oxidatihe trash . Chemicas garden  and invos garden  and flavThe existence of chemical gradients in the fungus garden resembles a predigestion process where substrates are modified to facilitate their consumption by the ants and residues are generated for removal as trash, as exemplified here by plant constituents that pass through an ant fungus garden ecosystem. Our study enabled us to detect molecular families and chemical transformations occurring in a fungus garden. These results will enable further investigations concerning the roles of these metabolites, as has been recently demonstrated for fatty acids in fungus gardens  and for A. texana fungus garden. Complementary to other studies exploring the capacity of fungus gardens to metabolize plant substrates  provided every 3 days. An empty plastic chamber was also provided that ants used to contain their waste (A queenright boratory . Colony ir waste .http://www.meshlab.net). The piece of fungus garden was then sliced into four layers, and each layer was further divided into nine pieces of approximately 3 by 3 by 3 cm (or 27 cm3) and homogenized by vortexing. Approximately 100 mg of each sample was extracted three times with 2:1 dichloromethane (DCM)-methanol (MeOH), sonicated for 10 min, dried under a stream of gaseous nitrogen, and shipped to the Dorrestein Laboratory at UC San Diego for LC-MS/MS acquisition. A short video showing the spatial distribution of the detected molecules from the deconstructed Atta texana fungus garden can be accessed at https://youtu.be/_ikhKelfrY8. Briefly, using the table of feature abundances containing the LC-MS/MS data (see “Feature-based molecular networking” described below) and the virtual 3D model, we show the distribution of each molecular signature detected in the fungus garden using ′ili for visualization (https://ili.embl.de/).A 10- by 10- by 10-cm piece of fungus garden was removed from the chamber and scanned for creation of a virtual 3D image using a Structure Sensor Mark II and the Structure app . The virtual 3D model was created using Meshmixer software , and the coordinates for ′ili visualization were obtained using MeshLab software (E)-9-oxo-11-(3-pentyl-2-oxiranyl)-10-undecenoic acid (compound 3) , phytosphingosine (compound 4) (Sigma-Aldrich), kaempferol  The detected feature corresponding to quercetin-3-O-pentoside, node inside the red circle , is highly abundant in the plant material. The annotation of quercetin-3-O-pentoside from GNPS libraries (cosine 0.96) corresponds to a level 3, as the stereochemistry of the sugar moiety cannot be determined by MS/MS data. (b) The detected feature corresponding to quercetin, the node inside the red circle (m/z 303.0503), is highly abundant in the plant material as well as in the top layer of the fungus garden. The annotation of quercetin from GNPS libraries (cosine 0.97) was confirmed by using a reference standard . Download FIG S5, TIF file, 1.0 MB.Molecular cartography of quercetin-3-Copyright © 2021 Caraballo-Rodríguez et al.2021Caraballo-Rodríguez et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 18 reversed-phase ultrahigh-performance liquid chromatography (UHPLC) column (50 by 2.1 mm) and Maxis Q-time of flight (TOF) mass spectrometer  equipped with an electrospray ionization (ESI) source. The column was equilibrated with 5% of solvent B  for 1 min, followed by a linear gradient from 5% to 100% of solvent B over 8 min, and then held at 100% solvent B for 2 min. Then, the gradient was reduced from 100% to 5% of solvent B over 0.5 min and then maintained at 5% solvent B for 2.5 min, using a flow rate of 0.5 ml/min throughout the run. MS spectra were acquired in positive ion mode in the range of 100 to 2,000 m/z. A mixture of 10 mg/ml each of sulfamethazine, sulfamethizole, sulfachloropyridazine, sulfadimethoxine, amitriptyline, and coumarin was run after every 96 injections for quality control. An external calibration with ESI-low concentration tuning mix  (Agilent technologies) was performed prior to data collection. An internal calibrant hexakisphosphazene (CAS 186817-57-2) was used throughout the runs. A capillary voltage of 4,500 V, nebulizer gas pressure (nitrogen) of 2 bar, ion source temperature of 200°C, dry gas flow of 9 liters/min source temperature, spectral rate of 3 Hz for MS1 and 10 Hz for MS2 was used during each run. For acquiring MS/MS fragmentation, the 5 most intense ions per MS1 were selected, the MS/MS active exclusion parameter was enabled, set to 2 and to release after 30 s, and the precursor ion was reconsidered for MS/MS if the current intensity/previous intensity ratio was >2. An advanced stepping function was used to fragment ions according to the settings in Samples were resuspended in 100% methanol containing 2 μM sulfamethazine as an internal standard, and LC-MS/MS analysis was performed in an UltiMate 3000 ultraperformance liquid chromatography (UPLC) system (Thermo Scientific) using a Kinetex 1.7-μm CFeature finding was performed with the open source MZmine software  version https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=c7a1733750bd415fa32176e125fac42e). An FDR of <0.16 was obtained for the obtained annotations using a minimum matching fragment ions of 5 (GNPS job link [Passatutto] https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=206023ccdbd747ee885aab2b16dfcf31).The Passatutto decoy-based method 43 was used to estimate an FDR for the annotations with the library match settings used for spectral identification. An FDR pf <0.072 was obtained for the annotations using a minimum matching fragment ions of 6  on the GNPS website (http://gnps.ucsd.edu). The data were filtered by removing all MS/MS fragment ions within ±17 Da of the precursor m/z. MS/MS spectra were window filtered by choosing only the top 6 fragment ions in the ±50-Da window throughout the spectrum. The precursor ion mass tolerance was set to 0.02 Da and a MS/MS fragment ion tolerance of 0.02 Da. A network was then created where edges were filtered to have a cosine score of >0.7 and more than 6 matched fragment ions. Furthermore, edges between two nodes were kept in the network if and only if each of the nodes appeared in each other’s respective top 10 most similar nodes. Finally, the maximum size of a molecular family was set to 100, and the lowest scoring edges were removed from molecular families until the molecular family size was below this threshold. The spectra in the network were then searched against GNPS’ spectral libraries. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score of >0.7 and at least 6 matched fragment ions. Molecular networks were visualized using Cytoscape (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=5df1dc83e075478ba69d1bb41bf9499a).A molecular network was created with the feature based molecular networking workflow , Super Natural II (https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=05f63922be7244bdb9aa42784be0a6eb). Automatic workflows for peptide analogues was performed using VarQuest (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=cba438bedbb04f73afd4bd7a85ef665d.Network annotation propagation (NAP)  was perftural II , GNPS, atural II   (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=50dfaf589c8140bd85a8ca199db59de3).Molecular network annotations were enhanced via the MolNetEnhancer workflow  merged ion (NAP) , VarQueson (NAP) , and MS2on (NAP)  to proviNs1 and Ms1 correspond to the peak area of the detected features N and M in sample S1, while Ns2 and Ms2 correspond to the peak area of the detected features N and M in sample S2. A constant (k = 1.0 e−10) is added to each value to avoid including any zero values during the calculation. Chemical proportionality scores are available within the GNPS environment and can be accessed through the following links: https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=059d652858134586a5320dc94e2d732e  and https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=da94680c01594122b7ef88a1070155ff .The proportionality score was calculated between two directly connected nodes across the entire molecular network using the following equation:12H14N4O2S) corresponding to m/z 279.0910 and with a retention time of 2.55 min. A cube root transformation was performed to facilitate feature comparison  can be accessed at https://gnps.ucsd.edu/ProteoSAFe/result.jsp?task=df2eb5792c84460b9413fa22af2d0d89&view=advanced_view.The data sets used in this work were deposited in the online repository GNPS/MassIVE. The data set corresponding to the molecular cartography of