Journal of Chemical Information and Modeling 最新文献

{"title":"Kinase-Bench: Comprehensive Benchmarking Tools and Guidance for Achieving Selectivity in Kinase Drug Discovery.","authors":"Tian-Hua Wei, Shuang-Shuang Zhou, Xiao-Long Jing, Jia-Chuan Liu, Meng Sun, Zong-Hao Zhao, Qing-Qing Li, Zi-Xuan Wang, Jin Yang, Yun Zhou, Xue Wang, Cheng-Xiao Ling, Ning Ding, Xin Xue, Yan-Cheng Yu, Xiao-Long Wang, Xiao-Ying Yin, Shan-Liang Sun, Peng Cao, Nian-Guang Li, Zhi-Hao Shi","doi":"10.1021/acs.jcim.4c01830","DOIUrl":"10.1021/acs.jcim.4c01830","url":null,"abstract":"<p><p>Developing selective kinase inhibitors remains a formidable challenge in drug discovery because of the highly conserved structural information on adenosine triphosphate (ATP) binding sites across the kinase family. Tailoring docking protocols to identify promising kinase inhibitor candidates for optimization has long been a substantial obstacle to drug discovery. Therefore, we introduced \"Kinase-Bench,\" a pioneering benchmark suite designed for an advanced virtual screen, to improve the selectivity and efficacy of kinase inhibitors. Our comprehensive data set includes 6875 selective ligands and 422,799 decoys for 75 kinases, using extensive bioactivity and structural data from the ChEMBL database and decoys generated by the Directory of Useful Decoys-Enhanced version. Our benchmarking sets and retrospective case studies were designed to provide useful guidance in discovering selective kinase inhibitors. We employed a Glide High-Throughput Virtual Screen and Standard Precision complemented by three scoring functions and customized protein-ligand interaction filters that target specific kinase residue interactions. These innovations were successfully implemented in our virtual screen efforts targeting JAK1 inhibitors, achieving selectivity against its family member, TYK2. Consequently, we identified novel potential hits: Compound <b>2</b> (JAK1 IC₅₀: 980.5 nM, TYK2 IC₅₀: 4.5 μM) and the approved pan-AKT inhibitor Capivasertib (JAK1 IC₅₀: 275.9 nM, TYK2 IC₅₀: 10.9 μM). Using the Kinase-Bench protocol, both compounds demonstrated substantial JAK1 selectivity, making them strong candidates for further investigation. Our pharmaceutical results underscore the utility of tailored virtual screen protocols in identifying selective kinase inhibitors with substantial implications for rational drug design. Kinase-Bench offers a robust toolset for selective kinase drug discovery with the potential to effectively guide future therapeutic strategies effectively.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9528-9550"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142764599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From SMILES to Enhanced Molecular Property Prediction: A Unified Multimodal Framework with Predicted 3D Conformers and Contrastive Learning Techniques.","authors":"Long D Nguyen, Quang H Nguyen, Quang H Trinh, Binh P Nguyen","doi":"10.1021/acs.jcim.4c01240","DOIUrl":"10.1021/acs.jcim.4c01240","url":null,"abstract":"<p><p>We present a novel molecular property prediction framework that requires only the SMILES format as input but is designed to be multimodal by incorporating predicted 3D conformer representations. Our model captures comprehensive molecular features by leveraging both the sequential character structure of SMILES and the three-dimensional spatial structure of conformers. The framework employs contrastive learning techniques, utilizing InfoNCE loss to align SMILES and conformer embeddings, along with task-specific loss functions, such as ConR for regression and SupCon for classification. To address data imbalance, we incorporate feature distribution smoothing (FDS), a common challenge in drug discovery. We evaluated the framework through multiple case studies, including SARS-CoV-2 drug docking score prediction, molecular property prediction using MoleculeNet data sets, and kinase inhibitor prediction for JAK-1, JAK-2, and MAPK-14 using custom data sets curated from PubChem. The results consistently outperformed state-of-the-art methods, with ConR and FDS significantly improving regression tasks and SupCon enhancing classification performance. These findings highlight the flexibility and robustness of our multimodal model, demonstrating its effectiveness across diverse molecular property prediction tasks, with promising applications in drug discovery and molecular analysis.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9173-9195"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.","authors":"Francisco L Feitosa, Victoria F Cabral, Igor H Sanches, Sabrina Silva-Mendonca, Joyce V V B Borba, Rodolpho C Braga, Carolina Horta Andrade","doi":"10.1021/acs.jcim.4c01811","DOIUrl":"10.1021/acs.jcim.4c01811","url":null,"abstract":"<p><p>Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. <i>In vitro</i> assays support high-throughput screening, allowing for efficient detection of toxic substances while considerably reducing the need for animal testing. Additionally, AI-based Quantitative Structure-Activity Relationship (AI-QSAR) models enhance early stage predictions by assessing the cytotoxic potential of molecular structures, which helps prioritize low-risk compounds for further validation. We present a freely accessible web application designed for identifying potential cytotoxic compounds utilizing QSAR models. This application utilizes machine learning techniques and is built on a data set of approximately 90,000 compounds, evaluated against two cell lines, 3T3 and HEK 293. Users can interact with the app by inputting a SMILES representation, uploading CSV or SDF files, or sketching molecules. The output includes a binary prediction for each cell line, a confidence percentage, and an explainable AI (XAI) analysis. Cyto-Safe web-app version 1.0 is available at http://insightai.labmol.com.br/.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9056-9062"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MacGen: A Web Server for Structure-Based Macrocycle Design.","authors":"Zhihan Zhang, Dongliang Ke, Chengshan Jin, Weiyu Zhou, Xiaolin Pan, Yueqing Zhang, Xingyu Wang, Xudong Xiao, Changge Ji","doi":"10.1021/acs.jcim.4c01576","DOIUrl":"10.1021/acs.jcim.4c01576","url":null,"abstract":"<p><p>Macrocyclization is a critical strategy in rational drug design that can offer several advantages, such as enhancing binding affinity, increasing selectivity, and improving cellular permeability. Herein, we introduce MacGen, a web tool devised for structure-based macrocycle design. MacGen identifies exit vector pairs within a ligand that are suitable for cyclization and finds 3D linkers that can align with the geometric arrangement of these pairs to form macrocycles. To aid in the fast acquisition of appropriate linkers, we have built an indexed 3D linker database that includes linkers of various lengths and categories. MacGen provides comprehensive configurable parameters that enable users to obtain preferred linkers, meeting unique requirements in practical ligand design scenarios. We hope MacGen will serve as a handy tool that can rapidly explore potential macrocycle space. The MacGen server is freely accessible at https://macgen.xundrug.cn.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9048-9055"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to \"Identification of a Novel Structural Class of HV1 Inhibitors by Structure-Based Virtual Screening\".","authors":"Martina Piga, Zoltan Varga, Adam Feher, Ferenc Papp, Eva Korpos, Kavya C Bangera, Rok Frlan, Janez Ilaš, Jaka Dernovšek, Tihomir Tomašič, Nace Zidar","doi":"10.1021/acs.jcim.4c02211","DOIUrl":"10.1021/acs.jcim.4c02211","url":null,"abstract":"","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9651"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.","authors":"Jordy Schifferstein, Andrius Bernatavicius, Antonius P A Janssen","doi":"10.1021/acs.jcim.4c01260","DOIUrl":"10.1021/acs.jcim.4c01260","url":null,"abstract":"<p><p>Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, resulting in risks for toxicity and general off-target effects. Assessing the binding of an inhibitor for the entire kinome is experimentally possible but expensive. A reliable and interpretable computational prediction of kinase selectivity would greatly benefit the inhibitor discovery and optimization process. Here, we use machine learning on docked poses to address this need. To this end, we aggregated all known inhibitor-kinase affinities and generated the complete accompanying 3D interactome by docking all inhibitors to the respective high-quality X-ray structures. We then used this resource to train a neural network as a kinase-specific scoring function, which achieved an overall performance (<i>R</i>²) of 0.63-0.74 on unseen inhibitors across the kinome. The entire pipeline from molecule to 3D-based affinity prediction has been fully automated and wrapped in a freely available package. This has a graphical user interface that is tightly integrated with PyMOL to allow immediate adoption in the medicinal chemistry practice.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9196-9204"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug-Target Interaction Prediction.","authors":"Jonghyun Lee, Dokyoon Kim, Dae Won Jun, Yun Kim","doi":"10.1021/acs.jcim.4c01397","DOIUrl":"10.1021/acs.jcim.4c01397","url":null,"abstract":"<p><p>Predicting drug-target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of the intricate and exceptionally lengthy protein target sequences. This study introduces MMF-DTI, a lightweight model that uses multimodal fusion, to improve the generalizability of DTI predictions without sacrificing computational efficiency. The MMF-DTI model combines four distinct modalities: molecular sequence, molecular properties, target sequence, and target function description. This approach is noteworthy because it is the first to use natural language-based target function descriptions in predicting DTIs. The effectiveness of MMF-DTI has been confirmed through benchmark data sets, demonstrating its comparable performance in terms of generalizability, especially in scenarios with limited information about the drug or target. Remarkably, MMF-DTI accomplishes this using only half of the parameters and 17% of the VRAM compared with previous state-of-the-art models. This allows it to function even in constrained computational environments. The combination of performance and efficiency highlights the potential of multimodal data fusion in improving the overall applicability of models, providing promising opportunities for future drug discovery endeavors.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9215-9226"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142764688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Mechanism of Rac1b: An All-Atom Simulation Study of the Alternative Spliced Variant of Rac1 Small Rho GTPase.","authors":"Sofia Cresca, Angela Parise, Alessandra Magistrato","doi":"10.1021/acs.jcim.4c01376","DOIUrl":"10.1021/acs.jcim.4c01376","url":null,"abstract":"<p><p>The Rho GTPase family plays a key role in cell migration, cytoskeletal dynamics, and intracellular signaling. Rac1 and its splice variant Rac1b, characterized by the insertion of an Extraloop, are frequently associated with cancer. These small GTPases switch between an active GTP-bound state and an inactive GDP-bound state, a process that is regulated by specific protein modulators. Among them, the Guanine nucleotide exchange factor (GEF) protein DOCK5 specifically targets Rho GTPases, promoting their activation by facilitating the exchange of GDP for GTP. In this study, we performed cumulative 10-μs-long all-atom molecular dynamics simulations of Rac1 and Rac1b, in isolation and in complex with DOCK5 and ELMO1, to investigate the impact of the Rac1b Extraloop. Our findings reveal that this Extraloop decreases the GDP residence time as compared to Rac1, mimicking the effect of accelerated GDP/GTP exchange induced by DOCK5. Furthermore, both Rac1b Extraloop and the ELMO1 protein stabilize the GTPase/DOCK5 complex, contributing to facilitate GDP dissociation. This shifts the balance between the GPT- and GDP-bound state of Rac1b toward the active GTP-bound state, sending a prooncogenic signal. Besides broadening our understanding of the biological functions of small Rho GTPases, this study provides key information to exploit a previously unexplored therapeutic niche to counter Rac1b-associated cancer.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9474-9486"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.","authors":"Jessica Valero-Rojas, Camilo Ramírez-Sánchez, Laura Pacheco-Paternina, Paulina Valenzuela-Hormazabal, Fernanda I Saldivar-González, Paula Santana, Janneth González, Tatiana Gutiérrez-Bunster, Alejandro Valdés-Jiménez, David Ramírez","doi":"10.1021/acs.jcim.4c01481","DOIUrl":"10.1021/acs.jcim.4c01481","url":null,"abstract":"<p><p>Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these challenges by providing a comprehensive, free web-based tool for parallel ligand-based virtual screening and network pharmacology, specifically targeting over 85 key proteins implicated in AD. This innovative approach is designed to enhance the identification and analysis of potential multitarget ligands, thereby accelerating the development of effective therapeutic strategies against AD. AlzyFinder Platform incorporates machine learning models to facilitate the ligand-based virtual screening process. These models, built with the XGBoost algorithm and optimized through Optuna, were meticulously trained and validated using robust methodologies to ensure high predictive accuracy. Validation included extensive testing with active, inactive, and decoy molecules, demonstrating the platform's efficacy in distinguishing active compounds. The models are evaluated based on balanced accuracy, precision, and F1 score metrics. A unique soft-voting ensemble approach is utilized to refine the classification process, integrating the strengths of individual models. This methodological framework enables a comprehensive analysis of interaction data, which is presented in multiple formats such as tables, heat maps, and interactive Ligand-Protein Interaction networks, thus enhancing the visualization and analysis of drug-protein interactions. AlzyFinder was applied to screen five molecules recently reported (and not used to train or validate the ML models) as active compounds against five key AD targets. The platform demonstrated its efficacy by accurately predicting all five molecules as true positives with a probability greater than 0.70. This result underscores the platform's capability in identifying potential therapeutic compounds with high precision. In conclusion, AlzyFinder's innovative approach extends beyond traditional virtual screening by incorporating network pharmacology analysis, thus providing insights into the systemic actions of drug candidates. This feature allows for the exploration of ligand-protein and protein-protein interactions and their extensions, offering a comprehensive view of potential therapeutic impacts. As the first open-access platform of its kind, AlzyFinder stands as a valuable resource for the AD research community, available at http://www.alzyfinder-platform.udec.cl with supporting data and scripts accessible via GitHub https://github.com/ramirezlab/AlzyFinder.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9040-9047"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NCAP: Noncanonical Amino Acid Parameterization Software for CHARMM Potentials.","authors":"Richard E Overstreet, Dennis G Thomas, John R Cort","doi":"10.1021/acs.jcim.4c00986","DOIUrl":"10.1021/acs.jcim.4c00986","url":null,"abstract":"<p><p>Noncanonical amino acids (ncAAs) provide numerous avenues for the introduction of novel functionality to peptides and proteins. ncAAs can be incorporated through solid-phase synthesis or genetic code expansion in conjugation with heterologous expression of the encoded protein modification. Due to the difficulty of synthesis or overexpression, wide chemical space, and lack of empirically resolved structures, modeling the effects of ncAA mutation is critical for rational protein design. To evaluate the structural and functional perturbations ncAAs introduce, we utilize molecular potentials that describe the forces in the protein structure. Most potentials such as CHARMM are designed to model canonical residues but can be parametrized to include novel ncAAs. In this work, we introduce NCAP, a software package to generate CHARMM-compatible parameters from quantum chemical calculations. Unlike currently available tools, NCAP is designed to recognize the ncAA structure and automatically bridge the gap between quantum chemical calculations and CHARMM potential parameters. For our software, we discuss the workflow, validation against canonical parameter sets, and comparison with published ncAA-protein structures.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9424-9432"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}