Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.

Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.

Drug target inference by mining transcriptional data using a novel graph convolutional network framework.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.

Discovery of Pyrazolo[3,4-d]pyridazinone Derivatives as Selective DDR1 Inhibitors via Deep Learning Based Design, Synthesis, and Biological Evaluation.

Alterations of discoidin domain receptor1 (DDR1) may lead to increased production of inflammatory cytokines, making DDR1 an attractive target for inflammatory bowel disease (IBD) therapy. A scaffold-based molecular design workflow was established and performed by integrating a deep generative model, kinase selectivity screening and molecular docking, leading to a novel DDR1 inhibitor compound 2, which showed potent DDR1 inhibition profile (IC50 = 10.6 ± 1.9 nM) and excellent selectivity against a panel of 430 kinases (S (10) = 0.002 at 0.1 µM). Compound 2 potently inhibited the expression of pro-inflammatory cytokines and DDR1 autophosphorylation in cells, and it also demonstrated promising oral therapeutic effect in a dextran sulfate sodium (DSS)-induced mouse colitis model.

Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.

The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90 and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted "off-target" activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.

Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery data sets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of data sets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns nonlocal intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.

KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules.

MOTIVATION: The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. RESULTS: KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases. AVAILABILITY AND IMPLEMENTATION: KinomeX is available at: https://kinome.dddc.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family.

The (S)-adenosyl-L-methionine (SAM)-dependent methyltransferases play essential roles in post-translational modifications (PTMs) and other miscellaneous biological processes, and are implicated in the pathogenesis of various genetic disorders and cancers. Increasing efforts have been committed toward discovering novel PTM inhibitors targeting the (S)-Adenosyl-L-methionine (SAM)-binding site and the substrate-binding site of methyltransferases, among which virtual screening (VS) and structure-based drug design (SBDD) are the most frequently used strategies. Here, we report the development of a target-specific scoring model for compound VS, which predict the likelihood of the compound being a potential inhibitor for the SAM-binding pocket of a given methyltransferase. Protein-ligand interaction characterized by Fingerprinting Triplets of Interaction Pseudoatoms was used as the input feature, and a binary classifier based on deep neural networks is trained to build the scoring model. This model enhances the efficiency of the existing strategies used for discovering novel chemical modulators of methyltransferase, which is crucial for understanding and exploring the complexity of epigenetic target space.

Preclinical characterization of anlotinib, a highly potent and selective vascular endothelial growth factor receptor-2 inhibitor.

Abrogating tumor angiogenesis by inhibiting vascular endothelial growth factor receptor-2 (VEGFR2) has been established as a therapeutic strategy for treating cancer. However, because of their low selectivity, most small molecule inhibitors of VEGFR2 tyrosine kinase show unexpected adverse effects and limited anticancer efficacy. In the present study, we detailed the pharmacological properties of anlotinib, a highly potent and selective VEGFR2 inhibitor, in preclinical models. Anlotinib occupied the ATP-binding pocket of VEGFR2 tyrosine kinase and showed high selectivity and inhibitory potency (IC50 <1 nmol/L) for VEGFR2 relative to other tyrosine kinases. Concordant with this activity, anlotinib inhibited VEGF-induced signaling and cell proliferation in HUVEC with picomolar IC50 values. However, micromolar concentrations of anlotinib were required to inhibit tumor cell proliferation directly in vitro. Anlotinib significantly inhibited HUVEC migration and tube formation; it also inhibited microvessel growth from explants of rat aorta in vitro and decreased vascular density in tumor tissue in vivo. Compared with the well-known tyrosine kinase inhibitor sunitinib, once-daily oral dose of anlotinib showed broader and stronger in vivo antitumor efficacy and, in some models, caused tumor regression in nude mice. Collectively, these results indicate that anlotinib is a well-tolerated, orally active VEGFR2 inhibitor that targets angiogenesis in tumor growth, and support ongoing clinical evaluation of anlotinib for a variety of malignancies.

Discovery of novel BET inhibitors by drug repurposing of nitroxoline and its analogues.

The BET family of bromodomain-containing proteins (BRDs) is believed to be a promising drug target for therapeutic intervention in a number of diseases including cancer, inflammation and cardiovascular diseases. Hence, there is a great demand for novel chemotypes of BET inhibitors. The drug repurposing strategy offers great benefits to find inhibitors with known safety and pharmacokinetic profiles, thus increasing medicinal chemists' interest in recent years. Using the drug repurposing strategy, a BRD4-specific score based virtual screening campaign on an in-house drug library was conducted followed by the ALPHA screen assay test. Nitroxoline, an FDA-approved antibiotic, was identified to effectively disrupt the interaction between the first bromodomain of BRD4 (bromodomain-containing protein 4) and acetylated H4 peptide with IC50 of 0.98 µM. Nitroxoline inhibited all BET family members with good selectivity against non-BET bromodomain-containing proteins, thus it is defined as a selective BET inhibitor. Based on the crystal structure of the nitroxoline-BRD4_BD1 complex, the mechanism of action as well as BET specificity of nitroxoline were determined. Since the anticancer activity of nitroxoline against MLL leukemia, one of the BET related diseases, has not been studied before, we tested whether nitroxoline might serve as a potential repurposing drug candidate for MLL leukemia. Nitroxoline effectively inhibited the proliferation of MLL leukemia cells by inducing cell cycle arrest and apoptosis. The profound efficacy is, at least in part, due to the inhibition of BET and downregulation of target gene transcription. Our discovery of nitroxoline as a BET inhibitor suggests potential application of nitroxoline and its derivatives for clinical translation in BET family related diseases.