Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates training a protein-specific model, which is also considered a learning task, and each task uses graph neural networks (GNNs) to learn the protein graph embedding and the molecular graph embedding. Inspired by the fact that molecules bind to a binding pocket in proteins instead of the whole protein, ZeroBind introduces a weakly supervised subgraph information bottleneck (SIB) module to recognize the maximally informative and compressive subgraphs in protein graphs as potential binding pockets. In addition, ZeroBind trains the models of individual proteins as multiple tasks, whose importance is automatically learned with a task adaptive self-attention module to make final predictions. The results show that ZeroBind achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs, and performs well after fine-tuning for those proteins or drugs with a few known binding partners. Existing drug-target interaction (DTI) prediction methods generally fail to generalize well unseen proteins and drugs. Here the authors report a protein-specific meta-learning framework, ZeroBind, with subgraph matching for predicting protein-drug interactions from their structures. However, the traditional experimental methods for resolving the crystal structures of drug-protein complexes to identify the drug-target interactions are costly and time-consuming5. In order to reduce costs, in silico approaches are gaining more attention. Instead of taking massive candidates into an in vitro search, it is more efficient and less costly to use computational approaches to virtually screen out most candidates prior to an in vitro search. Generally, there are two major groups of in silico approaches, docking simulations and data-driven learning-based methods. Docking simulations utilize the 3D structure of drug molecules and target proteins to identify their potential binding sites, which are also time-consuming7. In contrast, due to the rapid development of machine learning, utilizing the features derived from proteins and drugs to identify their interactions achieves both high accuracy and low cost.Identifying the interactions between drugs and targets (proteins) plays a crucial role in the process of drug discovery11, where interacting pairs of proteins and drugs are extracted from existing databases like BindingDB12, CHEMBL13, PDBbind15, and DrugBank16. Since the nature of potency values is logarithmic, a decrease in kinetic constants from micromolar to nanomolar levels results in an exponential change. Thus, the output values of regression tasks are normally defined as the negative log of kinetic constants, i.e., 18. Machine learning-based methods from molecules and proteins features mainly focus on learning a good representation of molecules and proteins, which are then fed into a classification/regression model to perform the prediction task19.Data-driven learning methods generally formulate drug-target interaction (DTI) prediction as binary classification or regression tasks20. Similarity/distance-based23 and network-based26 methods, which utilize protein-protein similarity, drug-drug similarity and known DTIs, achieve overestimated performance on the transductive test9, where both proteins and ligands in the test set are present in the training set. In addition, there are often few known binding drugs with a newly discovered target protein, making it difficult to work on the inductive test, where both proteins and ligands in the test set are absent in the training set. Currently, most existing methods focus on how to effectively learn representations of molecules and proteins, and then feed the representations into classification or regression models19. The representation learning methods, like DeepPurpose8, take molecular fingerprints and SMILES27 strings as the molecular features and amino acid sequences as protein features. Then they apply a convolutional neural network (CNN)28, recurrent neural network (RNN)29 or Transformer30 model to embed the input features. To take spatial information into consideration, some methods apply CNNs to 3D images derived from structures of proteins and molecule31. GNN32 has gained much attention in recent years due to its capability of handling graphs as non-Euclidean structured data33. Other methods utilize GNNs to embed graphs of proteins and molecules to perform DTI prediction10. DTI prediction also benefits from the rapid development of GNNs since both drug and target protein are naturally graph-structured data. GEFA34 combines pre-trained protein embeddings and a graph-in-graph neural network with an attention mechanism to capture the interactions between drugs and protein residues. Thus, it is reasonable to use GNNs for the representation learning of proteins and drugs.Recently, deep learning has achieved exciting results in DTI prediction by learning from known drug-target interactions. However, these methods cannot generalize well to those unseen proteins and drugs35 that remember the degree ratio of binding annotations in the training set rather than learning the molecular features of interactions9. To generalize to unseen proteins and drugs, AI-Bind leverages network-derived negatives and pre-training to resolve the shortcut learning issue9. Furthermore, proteins may have diverse binding patterns with drugs, which can be captured by training a protein-specific model for individual proteins. Recently, a ligand-based method MetaDTA36 is developed as a few-shot predictor for proteins with a few known binding drugs under the meta-learning framework. However, MetaDTA does not support zero-shot prediction for unseen proteins without known binding drugs in the training set.However, classical pair-input approaches often take drug-protein pairs as training samples by concatenating and feeding their representations into a dense layer to identify the interactions. However, these approaches may fail at inductive tests for unseen proteins and drugs not seen during the model training, which is due to their potential shortcuts37 as the training strategy and each base model makes binding drug predictions of a specific protein, which mainly consists of four modules: (1) a Graph Convolutional Network (GCN) encoder learning the embeddings of the molecule graph and protein graph, (2) a Subgraph Information Bottleneck (SIB) module generating the essential IB-subgraph of the protein graph as a potential binding pocket, (3) a Multilayer Perceptron (MLP) module concatenating the protein IB-subgraph embedding and molecular embedding to perform DTI prediction, and (4) a task adaptive self-attention module to measure the importance of different tasks, where different DTI tasks contribute differently to the meta-learner. The generated task weight is used for weighted average loss and further incorporated into the meta-learning procedure.To address these challenges, we propose a protein-specific zero-shot predictor ZeroBind for drug-target interaction prediction under the meta-learning framework. ZeroBind adopts the meta-learning framework MAML++In summary, our contributions are as follows: (1) We formulate the drug-target interaction prediction for unseen drugs and proteins as a zero-shot learning problem. The general knowledge of DTI prediction learned from existing proteins, drugs, and their interactions with meta-learning strategy have a greater ability for generalization to unseen proteins and drugs than existing methods. (2) We train one DTI task model per protein, where task adaptive self-attention is designed to calculate the contributions of multiple DTI tasks to the protein-specific meta-learner. Thus, each protein-specific model captures individual binding patterns to drugs. (3) We propose model-agnostic IB-subgraph learning to automatically discover compressed subgraphs as potential binding pockets in proteins instead of redundant graph information derived from the whole protein. (4) We conduct extensive experiments on three independent zero-shot test sets and one few-shot test set. Results show that ZeroBind consistently outperforms existing methods. Further validation of real-world SARS-COV-2 drug-target binding prediction demonstrates the reliability of ZeroBind predictions and the subgraphs detected by IB-subgraph learning align well with the known binding pockets in proteins.In this study, ZeroBind formulates the DTI prediction as a meta-learning task and proposes a meta-learning framework to solve the generalization problem of unseen proteins and drugs in DTI prediction. Specifically, a meta-learning task is defined as binding drug predictions of a specific protein, where IB-subgraph learning is leveraged to automatically discover compressed subgraphs as potential binding pockets in proteins and a self-attention mechanism is designed to learn weights for each task of a protein. The flowchart of ZeroBind is illustrated in Fig. 9 as data augmentation to alleviate the annotation imbalance  and the area under the precision-recall curve (AUPRC) on three independent test sets and one few-shot test set. To ensure the effectiveness of performance comparison, we conduct five independent experiments with five random seeds for datasets partitioning and model training, and report the average results along with standard deviation.−6, 8.02 × 10−11, 3.06 × 10−7, respectively. Moreover, the relative improvements of AUPRC are 1.00% on the Transductive test set, 0.96% on the Semi-inductive test set, and 1.21% on the Inductive test set compared to the best baseline method, the corresponding t-test p-value is 8.93 × 10−8, 5.15 × 10−3, 1.56 × 10−5, respectively. In addition, on the three test sets, we observe that the performance of all methods decreases, indicating that a certain external shortcut learning exists for the Transductive test set.The overall performances of all methods in three independent test sets are reported in Fig. Figure We also evaluate the protein-specific DTI prediction performance of ZeroBind with other baseline methods on the inductive test set and semi-inductive test set Fig. . After eWe further evaluate ZeroBind against the baseline methods on the few-shot test set. ZeroBind and other baseline methods are first trained with the training set as the pre-training model, and further fine-tuned on each few-shot fine-tuning set and evaluated on the corresponding few-shot test set. The performance of ZeroBind model and other baselines on the few-shot test set is shown in Fig. 38 to compare the predicted binding pocket against the true binding pocket on PDBbind dataset15. The true binding pocket information containing pocket residues and 3D coordinate locations are downloaded from PDBbind, which contains 14,000 binding residues for 535 proteins. The Jaccard similarity coefficient is used to calculate the degree of intersection between two sets of individual DTIs, and the formula is defined as ZeroBind does not use binding pocket information as ground truth labels for model training, instead, it uses the global DTI labels as a weakly supervised labels. To demonstrate that the IB-subgraph module in ZeroBind is able to detect the binding pocket in the protein, we use the Jaccard similarity coefficientIn addition, we calculate the Jaccard similarity coefficient of predicted binding pockets nodes and the first-order neighbors of the true binding pocket nodes as random. The results are shown in the ablation studies and here we also calculate the Jaccard similarity coefficients of randomly sampled binding residues with true binding pockets and the first-order neighbors of true binding pockets, respectively. The ZeroBindrandom yields an average Jaccard similarity coefficients of 0.013 and 0.072 , which is much smaller than that of ZeroBind, respectively. As shown in Fig. Figure To further demonstrate the effectiveness of the IB-subgraph module, we train a variant ZeroBind for those proteins with known binding pockets using true binding pocket as the node assignment matrix 39.We further visualize the generated IB-subgraph as the potential binding pocket for Serine/threonine-protein kinase N1 protein in Fig. 40, where the PDB IDs for SARS-COV-2 protein structures are given in Supplementary Table 41.To better demonstrate the effectiveness of ZeroBind, we use Auto-Docking simulations to validate its predicted potential drugs targeting the SARS-COV-2 proteins. Auto-Docking simulation is a time-consuming but reliable tool to simulate the drug-target binding process. To respond quickly to public health emergencies, we need to validate ZeroBind on proteins that don’t have many binding molecules. Thus, we apply ZeroBind to predict the interactions between the structures of 10 SARS-CoV-2 viral proteins not in the training set and 10,000 drugs in PubChem42. As demonstrated in9, the average binding affinities are close to −7.5 kcal/mole for three binding pairs of SARS-COV-2 proteins and drugs. Furthermore, we simulate the drug-target binding complex between ORF8 accessory protein and VZBSCWDKCMOJCR-UHFFFAOYSA-N drug in Fig. The average binding affinity of the top-10 predicted pairs by ZeroBind is −7.42 kcal/mole Fig. , which aMAML-: Train the base model of ZeroBind directly without the meta-learning strategy.ZeroBindSIB-: Using all node embeddings of the protein to identify the interaction instead of applying a SIB module to find the IB-graph on the protein graph.ZeroBindattention-: ZeroBind without task adapted attention module to balance the importance of different tasks.ZeroBindGIN: ZeroBind uses GIN instead of GCN as the backbone GNN.ZeroBindrandom: ZeroBind sets the node assignment matrix ZeroBindZ in Eq.  randomlyTo demonstrate the added value of individual modules in ZeroBind, we also conducted an ablation study to evaluate the effectiveness. The details of ZeroBind with different modules are illustrated as follows:MAML-, we observe a significant decrease in the semi-inductive and inductive test sets, demonstrating that the meta-learning training strategy provides the powerful generalization ability to unseen proteins and drugs. We can see that ZeroBindSIB- yields a lower performance without the SIB module to find the IB-subgraph, indicating the embedding of the whole protein is less effective than the subgraph embedding. The potential reason is that redundant graph information exists in the whole protein graph, further indicating that the molecule binds to a binding pocket instead of the whole protein. ZeroBindattention- performs slightly worse than ZeroBind, indicating the necessity of task adaptive self-attention module to balance different DTI tasks for different proteins. We also see a performance decline for ZeroBindsampling-, due to the lack of the value-generated nonbinding annotations by network-based negative samplings that is able to alleviate the potential short-cut learning. For ZeroBindGIN, there is no significant difference in the performance compared with ZeroBind, which indicates that the backbone GNN has a smaller impact on the performance than other modules in ZeroBind. Compared to randomly selected binding pockets, ZeroBindrandom performs worse than ZeroBind, further validating the effectiveness of the SIB module for detecting potential binding pockets. Moreover, we can see that ZeroBindrandom with randomly selected pockets performs worse than ZeroBindSIB- without the IB-subgraph module, indicating that initially inaccurate binding pockets guides the model to not locate true binding pockets, resulting in a wrong DTI prediction.The results are shown in Table The interaction of proteins with drug molecules is an important research topic, especially in the face of proteins and drug molecules not seen in the training set. Considering the information of proteins and molecules simultaneously is an under-explored idea to solve this problem. In this study, we formulate the drug-target interaction (DTI) prediction as a meta-learning task and propose a meta-learning framework called ZeroBind to solve the generalization problem of unseen proteins and drugs in DTI. Specifically, a meta-learning task is defined as binding drug predictions of a specific protein. The results demonstrate that ZeroBind outperforms existing methods in zero-shot and few-shot scenarios. In addition, the subgraphs learned by SIB align well with binding pockets in the protein, where randomly selected residues as binding pockets almost do not overlap with true binding pockets. Furthermore, we validate ZeroBind’s performance in a real-world scenario, where it predicts drug-target bindings for SARS-COV-2.Due to the rapid development of the research on GNNs, proteins and molecules can be encoded in a more natural form than sequences in previous studies. In addition, the meta-learning strategy also provides a more precise way of delineating the protein-specific DTI task space, which is also consistent with the experimental workflow for proteins in real drug experiments. However, ZeroBind also has some limitations, such as the difficulty of meta-learning training, where the training procedure is complex and prone to instability.The IB-subgraph method offers an interpretable ability of the model for understanding representation learning. In ZeroBind, the potential binding pockets are automatically detected with the weakly supervised IB-subgraph method that does not use binding pocket annotations as labels. To the best of our knowledge, still no published methods use IB-subgraph or other subgraph-based methods to identify potential binding pockets in the proteins, existing subgraph-based methods are focused on drug molecules. In the current study, we do not incorporate real binding pocket information into model training, since the binding pocket data is far less than the DTI data, and the protein structures predicted by AlphaFold2 still have no known binding pocket annotations. Of the proteins in the benchmark dataset, only 535 proteins have a part of known binding pockets. Thus, it is difficult to train the SIB module in ZeroBind for all proteins to detect binding pockets using the local binding pocket labels. Instead, we train the SIB module of ZeroBind using the global DTI binding labels, which potentially guides the SIB module to locate the potential binding pockets in proteins. As shown in our experiment, ZeroBind with true binding pockets yields better performance. A future update of ZeroBind is expected to take true binding pocket information into the training process to make a more accurate fitting to the DTI problem, if we can collect more true binding pocket data.43. In future work, we expect to investigate more advanced GNNs in ZeroBind.Furthermore, the base model in ZeroBind is GCN and there have been more advanced neural architectures for protein-molecule binding, such as SE(3)-equivariant GNN used in EquiBind12 is a public database of DTI interactions, which deposits binding affinity data between drugs (drug-like molecules) and target proteins. It currently contains over 2,600,000 experimentally determined binding affinities of protein-drug complexes between over 8000 protein targets and over 1,100,000 small molecules.BindingDB44, 1,500,000 protein-drug pairs were collected. We use the threshold in AI-bind9, which treats kinetic constants To create the training and test datasets for ZeroBind, we apply several filtering and preprocessing steps to create a high-quality benchmark dataset. First, data points are filtered with “single protein” for the “target type” attribute, and kinetic constants To demonstrate the effectiveness of ZeroBind, we construct three independent test sets for model evaluation through protein sequence similarity clustering by cd-hit and molecule scaffold split: (1) Transductive test set, where molecules with the same scaffold and proteins with the same cluster are in the training set, but their interactions do not exist in the training set; (2) Semi-inductive test set, where the proteins in the same cluster are in the training set, but the molecules with the same scaffold are not; (3) Inductive test set, where molecules with the same scaffolds and proteins in the same clusters are not in the training set.We first use cd-hit, a widely used program for clustering protein sequences, to cluster 1603 proteins into 1101 clusters with a similarity threshold 0.4. Through comparing molecule scaffolds, we split the molecules into the training molecule set and test molecule set with a training ratio of 0.95, and ensure that these two sets do not have overlapping scaffolds. As the meta-learning-based framework requires sufficient data, we first divide the clusters that have any protein with the number of associated molecules <20 into the test clusters, and the remaining clusters as the training clusters. Then, we construct the training set using 95% of the proteins in the training clusters and SMILES in the training molecules set. The remaining 5% of the proteins in the training clusters and smiles in the training molecules set are used as the Transductive test set. Finally, the proteins and smiles both in the test clusters and molecule set are further used as the Inductive test set, and the rest data are used as the Semi-inductive test set. During the model training, the cross-validation approach is applied for model optimization.In addition, we construct another few-shot test set with combining the Semi-inductive test set and the Inductive test set to evaluate the few-shot learning power of ZeroBind. Then, we randomly select 5 positive and 5 negative DTIs of each protein as the few-shot fine-tuning set, and the rest DTIs of each protein as the few-shot test set. Proteins that don’t have enough positive or negative DTIs are excluded from the few-shot fine-tuning set and the few-shot test set. The details of the training set and four test sets are given in Table 9 demonstrates that the annotation imbalance causes the network to learn topological shortcuts instead of the binding patterns of drug-target interactions. Similar to AI-Bind9, we use the network-based negative sampling of training datasets as data augmentation to alleviate the annotation imbalance. Specifically, we construct a bipartite drug-target network. The bipartite network is illustrated in Fig. We observe there exists data imbalance in the training set, and most proteins have a high positive ratio. AI-bindhttps://www.rcsb.org)45, and the rest 3D structures of 635 proteins are predicted by AlphaFold246. In addition, we download the true binding pocket information containing pocket residuals and pocket 3D coordinate locations from the PDBbind database, which contains binding pockets for 14,336 DTIs.The 3D structures of 996 proteins in this study are downloaded in PDB format from the RCSB Protein Data Bank  datasets47 to obtain molecular representation. Specifically, atom chemical features consist of atomic number, chirality, atomic formal charge, number of hydrogen atoms attached, radical electrons number, hybridization type, and aromatic. Geometric features consist of atom degree, and a binary value that whether in the ring is used for encoding node representation. Edge features consist of bond type, bond stereo and a binary value that whether bond is conjugated.After using RDkitDefinition 1: We define a drug as a graph denoted as 48, the two residue nodes are connected with one edge in the protein graph.Definition 2: We define a protein graph as 46 to predict their structures as complementary. The node features of the protein graph are initialized with pre-trained ESM-249 emebddings, a general-purpose protein language model, of the residues to incorporate prior knowledge. The edge features of the protein graph are the Euclidean distance between the pairs of nodes. The whole process of constructing a protein graph is shown in Fig. For proteins without known 3D structures, we use AlphaFold2In this work, we train a DTI task per protein.p is a protein in the protein set P and Mp is the drug set of its corresponding binding molecules and non-binding molecules.Definition 3: We define a DTI task Definition 4: We define the zero-shot DTI predicting task inherited from DTI task, which represents that the meta-model is directly used for prediction without fine-tuning with samples of the protein in meta-learning test set.Definition 5: We define the few-shot DTI predicting task inherited from DTI task, which uses the protein-specific model for prediction after fine-tuning with samples of the protein in meta-testing.The few-shot DTI prediction schedule and zero-shot DTI prediction schedule are shown in Fig. 50. There are two main types of meta-learning framework: (1) Gradient-based methods: The classic gradient-based method MAML51 uses a meta-learner to learn a good initialization by summing up multiple task losses and updating the parameters across tasks. Therefore, MAML could achieve both high accuracy and speed for generalization; (2) Metric-based methods: The prototypical network52 is a classic metric-based meta-learning algorithm that directly trains the vector representation  of each category. Once a good feature extractor is trained, the category of a new sample is determined by its closest prototype in the vector space. In our study, we apply gradient-based methods MAML as the training strategy. Besides, we follow MAML++37 to make some improvements to stabilize the training process of MAML.The meta-learning framework was originally proposed to learn general knowledge across multiple related tasks within a distribution and used this general experience to quickly adapt to additional tasks and improve prediction performanceθ. Given training tasks (proteins) sampled from the task distribution k data samples of each label for several inner steps to learn a task-specific model and then tests on m data samples. After obtaining the loss for each task, MAML applies the gradient descent with the sum or average of the loss across all tasks. Here we refer the training task and test task as the support set and query set, respectively. However, MAML suffers from instability during training and is sensitive to neural network architectures and a large number of hyperparameters. Therefore, we follow MAML++37 training procedure, which leverages multi-step loss optimization (MSL) and learning per-layer per-step learning rates and gradient directions (LSLR) in MAML.In the details of ZeroBind, several techniques including multi-step loss optimization (MSL), learning per-layer per-step learning rates and gradient directions (LSLR), subgraph information bottleneck (SIB), and task adaptive self-attention are applied to improve the performance of ZeroBind. We build the zero-shot learning framework based on MAML. First, we randomly initialize the network parameter Specifically, the multi-step loss optimization calculates the loss of the query set after each inner step, and optimizes the base network with the support set after all inner step optimization is completed. More formally, we optimize the model parameters as follows:The LSLR module sets the learning rate to be learnable parameters for each layer at each inner step. Without adding much computation, different learning rates are automatically learned for each layer at each step, which may help alleviate overfitting.To summarize the meta-learning procedure in ZeroBind Boxes  and 2, tBox 1 ZeroBind training processRequire: While not done do:Sample batch of tasks For all  Sample k examples as support sets of  Sample m examples as query sets of   For i=1 to inner steps do:  Calculate the inner step loss weight            End forEnd forEnd whileBox 2 BaseModel forward processRequire: For i=1 to num steps doFigure Graph neural network-based representation learning for proteins and drugs. After constructing drug and protein graphs for a given protein-drug pair, we first embed the molecule atom features to vector space with randomly initialized parameters. We denote the node embedding of the molecule as For protein graphs, we initialize the node embedding 53 to identify the most interpretable subgraph with the most crucial information associated with the DTI task, where the learned subgraph corresponds to the binding pocket on the protein graph. The SIB module is proposed to recognize a compressed subgraph named IB-subgraph under the information bottleneck (IB) principle. The IB-subgraph could also eliminate the noisy and redundant graph information. In our DTI task, the DTI score is largely determined by the protein pocket features, which are the subgraph information bottleneck of the protein graph or the potential drug binding sites in the protein.Subgraph learning in ZeroBind for potential binding pockets in proteins. Considering that a molecule binds to a binding pocket in the protein instead of the whole protein, after the protein backbone GCN, we apply a model-agnostic SIB moduleThe SIB module contains a subgraph generator for node assignment and a dense layer to guarantee that the generated subgraph matches the IB-subgraph. The subgraph generator is a Multi-layer Perceptron (MLP), which takes the protein node embedding and the molecule embedding as the input and outputs the node assignment matrix To stabilize the training process of the subgraph generation and separate Formally, the subgraph information bottleneck seeks the IB-subgraph by optimizing the following objective function:54 of the KL-divergence is applied to approximate the upper bound of To optimize the objective function, the lower bound of the first term is formulated as the opposite number of classification loss between the ground truth label and predicted label with subgraph embedding. By minimizing the classification loss, the lower bound of To minimize After obtaining the embedding of the generated protein IB-subgraph, we concatenate the molecular embedding and protein IB-subgraph embedding as the input to the MLP classification layer. The DTI classification loss could be formulated as:The overall loss can be formulated as:Here, we summarize the training process, the d by Eq. , represed in Eq. . To maxid in Eq.  and achis of Eq. . After ts of Eq. . By optimize Eq.  and seekIn our task, we aim to find the protein binding pocket under the weakly supervised training process, since the amount of protein binding pockets data is much smaller than the amount of binding data. So we design the IB-subgraph to find the protein binding pockets optimized on the DTI binding labels instead of binding pocket labels in proteins. Considering the protein pockets are determined by both proteins and molecules, we take the concatenation of molecule embedding and protein embedding as input when we generate the node assignment matrix Z in Eq. .In a traditional meta-learning schedule, ule Fig.  to learn36 uses multi-head cross-attention network to capture the relationships between the support and the query ligands instead of different proteins, where it does not use any protein information. In contrast, ZeroBind utilizes a task adaptive self-attention module to learn the importance for different tasks of proteins, and it focuses on the shared binding patterns among different proteins.MetaDTAWe take graph convolutional networks as base graph neural network. In our experiment, the GCN layer number of 11: DeepConv-DTI trains a CNN to learn the embedding of the neighboring residues and an MLP to learn the molecular fingerprint, which is concatenated to make DTI prediction.DeepConv-DTI10: GraphDTA leverages multiple types of GNN models to embed molecular embeddings and a CNN model to embed protein residue features.GraphDTA8: Deeppurpose is a state-of-art deep learning library for DTI prediction with multiple backbone networks. Here we use CNN as the backbone network for learning embeddings of both drugs and proteins.Deeppurpose9: AI-bind uses pre-trained mol2vec and protvec models to initialize the molecule embedding and protein embedding, and makes the DTI predictions by concatenating the molecule embedding and protein embedding.AI-bind55: DrugBAN uses a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn local pairwise interactions between drugs and targets.DrugBANWe compare ZeroBind with multiple baselines to evaluate its advantages, and calculate the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC) on three independent test sets and one few-shot test set.Further information on research design is available in the SupplementaryInformationPeer Review FileReporting SummarySource data


Severe acute respiratory syndrome–associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by  As the world is facing an outbreak of coronavirus disease 2019 caused by severe acute respiratory syndrome (SARS)–associated coronavirus 2 (SARS-CoV-2) for more than 2 years causing deaths of about six million and many more millions of infected cases , SARS haProteins perform a wide range of functions within organisms, including the structure of cells and organisms, and also participate in a variety of important processes in vivo through interactions with other molecules. Millions of proteins are still uncharacterized and therefore, unveiling the biological functions and characteristics of these uncharacterized proteins of different organisms is now a common practice in the fields of bioinformatics -28. SARShttps://www.ncbi.nlm.nih.gov) : Q6S8D9 (primary or citable), J9TE29 (secondary).The complete workflow of our study has shown in 356H573N93O96S5. In addition, the overall positively (Arg + Lys) and negatively (Asp + Glu) charged residues were 6 and 7 in numbers, respectively. The presence of Cys, Trp, and Tyr residues is indicated by a high Extinction coefficient of 8,730. The query protein has a higher aliphatic index value of 119.86, indicating that it is stable over a wide temperature range  areas, respectively, which validate our obtained tertiary structure. In addition, 6.5% and 5.9% residues were pitched in the additional allowed regions and 0.4% and 0.2% were found in the disallowed regions, respective of the servers. However, no residue was found in the generously allowed regions in the Phyre2 tool .In case of the Swiss-Model Interactive Workspace, another validating tool, 93.88% residues designated as the Ramachandran favored and the MolProbity Score calculated to be 1.82 which also positively evaluate the 3D structure of the hypothetical protein. Among the other features of the Swiss-Model Interactive Workplace, Z-scores of the QMEAN , Cβ, all atom pairwise, solvation energy, and the torsion angle value were found −1.76, −1.68, −0.78, −0.80 and −1.32, respectively, which also supported the proteins’ tertiary structure . FurtherCASTp v.3.0 , a sophiCharacterization of a protein using sophisticated bioinformatics tools is another novel task as like as other systems biology works. In our study, we aimed to reveal the physicochemical characteristics, structures and functions of a hypothetical protein Q6S8D9_SARS of SARS-CoV. The 70 amino acid containing protein contains more negatively charged residues and a high aliphatic index value and a low instability index value make the protein more temperature stable. The secondary structure modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was a functional protein and tertiary structure prediction showed the protein had a fine 3D structure validated by various servers. Moreover, active site was found in the protein where ligand could bind. Further study of the protein is needed to find novel therapeutic drug for the SARS-CoV treatment targeting the protein.


For over half a century, it has been known that protein molecules naturally undergo extensive structural fluctuations, and that these internal motions are intimately related to their functional properties. The energy landscape view has provided a powerful framework for describing the various physical states that proteins visit during their lifetimes. This Perspective focuses on the commonly neglected and often disparaged axis of the protein energy landscape: entropy. Initially seen largely as a barrier to functionally relevant states of protein molecules, it has recently become clear that proteins retain considerable conformational entropy in the “native” state, and that this entropy can and often does contribute significantly to the free energy of fundamental protein properties, processes, and functions. NMR spectroscopy, molecular dynamics simulations, and emerging crystallographic views have matured in parallel to illuminate dynamic disorder of the “ground state” of proteins and their importance in not only transiting between biologically interesting structures but also greatly influencing their stability, cooperativity, and contribution to critical properties such as allostery. The exquisite detail at the atomic scale of protein molecules derived from cryogenic x-ray crystallographyIronically perhaps, from the point of view of the residual entropy of protein molecules, it has turned out that the highly disordered unfolded state has arguably been more successfully characterized than the more structured states of proteins. Because of extensive averaging of various properties across the ensemble of structures comprising the unfolded state, both experiment and theory have led to rather satisfying descriptions.15N relaxation, and the free energy of ligand binding.The funnel of the energy landscape is often artistically rendered as having a sharp lowest energy state with accordingly little residual conformational entropy. This is often misread to mean something quite incorrect. The essential detail often overlooked by the casual reader is where kT crosses the funnel. The thermal energy is, of course, what ultimately defines to what degree various states of the protein will be occupied. Over the past decade or so, it has become apparent that kT is surprisingly high up the energy landscape, and that proteins retain a considerable amount of conformational entropy at physiological temperature. This conclusion comes largely from solution nuclear magnetic resonance (NMR), which is not only uniquely positioned to provide experimental evidence of internal motion but also of the attendant entropy that this motion represents.entire protein upon a change in functional state .For technical reasons, the NMR relaxation phenomena employed to measure motion require isolation of what will be a limited number of NMR probes within the protein.Initial measurements with calcium-activated calmodulin revealed a remarkably variable dynamic response of methyl-bearing side chain motion upon high affinity binding of various peptides corresponding to minimal calmodulin-binding domains of regulated proteins.The interaction of protein molecules with those of solvent has been extensively pursued by experiment and theory. It now seems clear that the relatively weak water–protein interactions nominally associated with the “hydrophobic effect” do not influence significantly the conformational entropy of proteins at physiological temperatures.Proteins are exquisitely clever machines and do amazing things. Even “simple” functions such as ligand binding are difficult to rigorously describe.