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Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

https://doi.org/10.1002/wcms.1225
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GFscore: A General Nonlinear Consensus Scoring Function for High-Throughput Docking
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NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
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Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation
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NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
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In-silico-based lead optimization of hit compounds targeting mitotic kinesin Eg5 for cancer management
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In-silico inquest reveal the efficacy of Usnea longissima ach. (A tropical Lichen) against ovarian cancer
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Modeling Protein-Ligand Interactions: Are We Ready for Deep Learning?
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Virtual screening of small-molecule libraries
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Structure-Based Virtual Screening: Theory, Challenges and Guidelines
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Best Practices for Docking-Based Virtual Screening
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Modern Tools and Techniques in Computer-Aided Drug Design
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Advances in structure-based virtual screening for drug discovery
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Methods and applications of machine learning in structure-based drug discovery
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Current applications of biomolecules in artificial intelligence and machine learning
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Aptamer and its selection via various SELEX strategies
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Computational Toxicology
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Artificial intelligence in early stages of structure-based drug discovery
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Exploring promising pharmaceutical candidates for gastrointestinal disorders using In silico approaches
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Use of molecular docking computational tools in drug discovery
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Experimental Uncertainty in Training Data for Protein-Ligand Binding Affinity Prediction Models
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Generative β-hairpin design using a residue-based physicochemical property landscape
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Recent progress on the prospective application of machine learning to structure-based virtual screening
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Machine learning concepts and its applications for prediction of diseases based on drug behaviour: An extensive review
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Current status and applications of Artificial Intelligence (AI) in medical field: An overview
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Machine learning optimization of cross docking accuracy
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Interaction prediction in structure-based virtual screening using deep learning
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Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends
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Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development
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Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions
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Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science
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A review on compound-protein interaction prediction methods: Data, format, representation and model
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On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach
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Machine learning in metastatic cancer research: Potentials, possibilities, and prospects
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Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening
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ProteoDockNet: Novel GNN-based ligand binding affinities prediction architecture via SMILES to key liver, kidney and brain proteins using QSAR data
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Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening
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Machine-learning methods for ligand–protein molecular docking
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Enhancing preclinical drug discovery with artificial intelligence
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Artificial intelligence in virtual screening: Models versus experiments
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New avenues in artificial-intelligence-assisted drug discovery
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Advances in molecular glues: exploring chemical space and design principles for targeted protein degradation
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PROTACs in targeted protein degradation: Advances in development and AI-enhanced drug discovery
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Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages
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Automatic selection of molecular descriptors using random forest: Application to drug discovery
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An improved algorithm optimization algorithm based on RungeKutta and golden sine strategy
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Citrinal B, natural 11 beta-hydroxysteroid dehydrogennase type 1 inhibitor identified from structure-based virtual screening
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AI in drug discovery and its clinical relevance
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An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions
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Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
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Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing
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PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions
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Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode
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Integration of machine learning classifiers and higher order tensors for screening the optimal recipe of filter media in stormwater treatment
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Identifying Potential Ageing-Modulating Drugs In Silico
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Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation
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TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction
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Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions
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Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
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End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design
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Application of Molecular Simulation Methods in Food Science: Status and Prospects
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GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds
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Development of a New Scoring Function for Virtual Screening: APBScore
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Toward a Computational Ecotoxicity Assay
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Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies
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Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space
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Improved Protein–Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
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Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
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Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning
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Improving Docking Power for Short Peptides Using Random Forest
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Lin_F9: A Linear Empirical Scoring Function for Protein–Ligand Docking
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Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
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Yuel: Improving the Generalizability of Structure-Free Compound–Protein Interaction Prediction
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Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein–Ligand Scoring Functions
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Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening
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The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking
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Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons
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PLANET: A Multi-objective Graph Neural Network Model for Protein–Ligand Binding Affinity Prediction
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A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules
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Open-Source Machine Learning in Computational Chemistry
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Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction
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Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
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CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein–Ligand Binding Interactions
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01260openalex-snapshot
Docking-Informed Machine Learning for Kinome-wide Affinity Prediction
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RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models
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CL-GNN: Contrastive Learning and Graph Neural Network for Protein–Ligand Binding Affinity Prediction
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GRADE and X-GRADE: Unveiling Novel Protein–Ligand Interaction Fingerprints Based on GRAIL Scores
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00427openalex-snapshot
Improved Prediction of Drug–Protein Interactions through Physics-Based Few-Shot Learning
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SMARTDock: A Toolkit for the Automated Development of Target-Specific Scoring Functions Using Bioactivity Data
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AttentionScore: A Target-Specific, Bias-Aware Scoring Function for Structure-Based Virtual Screening: A Case Study on METTL3
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00858openalex-snapshot
UniPoseScore: A Unified Graphormer3D-Based Framework for Protein–Ligand Binding Pose Scoring and Refinement
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01550openalex-snapshot
Native Contact Ratio as a Topological Metric for Machine Learning Based Molecular Docking
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Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein–Ligand Interactions
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K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
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Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning
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Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions
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Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
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Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring
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Scaffold Hopping Transformations Using Auxiliary Restraints for Calculating Accurate Relative Binding Free Energies
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Integrated Molecular Modeling and Machine Learning for Drug Design
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Characterizing Protein–Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease
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On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks
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Boosting Protein–Ligand Binding Pose Prediction and Virtual Screening Based on Residue–Atom Distance Likelihood Potential and Graph Transformer
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Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method
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Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2
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Machine-Learning- and Knowledge-Based Scoring Functions Incorporating Ligand and Protein Fingerprints
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Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein–Ligand Complexes in Cognate Docking
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graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
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Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark
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DrugRep: an automatic virtual screening server for drug repurposing
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Algebraic graph-assisted bidirectional transformers for molecular property prediction
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A practical guide to machine-learning scoring for structure-based virtual screening
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OptiPharm: An evolutionary algorithm to compare shape similarity
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Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem
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New machine learning and physics-based scoring functions for drug discovery
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3pHLA-score improves structure-based peptide-HLA binding affinity prediction
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AI-driven virtual screening platform identifies novel NSUN2 inhibitor candidates for targeted cancer therapy: a computational drug discovery approach
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A geometric deep learning approach to predict binding conformations of bioactive molecules
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Learning on topological surface and geometric structure for 3D molecular generation
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FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer
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Performance of machine-learning scoring functions in structure-based virtual screening
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Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning
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A vending machine for drug-like molecules – automated synthesis of virtual screening hits
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A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
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Survey of the scoring functions for protein-ligand docking
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Reinforcement learning: A novel approach towards drug discovery
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Knot data analysis using multiscale Gauss link integral
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Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors
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An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives
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RNA aptamer-mediated gene therapy of prostate cancer: lessons from the past and future directions
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Macromolecular target prediction by self-organizing feature maps
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Recent advances in automated protein design and its future challenges
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A public-private partnership for the express development of antiviral leads: a perspective view
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Artificial intelligence in drug discovery: recent advances and future perspectives
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Computational approaches for the design of modulators targeting protein-protein interactions
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Artificial intelligence for small molecule anticancer drug discovery
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Data-centric challenges with the application and adoption of artificial intelligence for drug discovery
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Beware of the generic machine learning-based scoring functions in structure-based virtual screening
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The impact of compound library size on the performance of scoring functions for structure-based virtual screening
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Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
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