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Combining machine‐learning and molecular‐modeling methods for drug‐target affinity predictions

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Multi‐state modeling of G‐protein coupled receptors at experimental accuracy

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Machine‐learning scoring functions for structure‐based virtual screening

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The MolSSI QCA rchive project: An open‐source platform to compute, organize, and share quantum chemistry data

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Machine Learning for In Silico ADMET Prediction

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Machine Learning in Drug Discovery: A Review

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Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution

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Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

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Recent progress on the prospective application of machine learning to structure-based virtual screening

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Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds

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Machine Learning guided early drug discovery of small molecules

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Machine learning-based approach: global trends, research directions, and regulatory standpoints

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BINANA: A novel algorithm for ligand-binding characterization

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Simulations meet machine learning in structural biology

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Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions

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PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties

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Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge

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DeepBSP—a Machine Learning Method for Accurate Prediction of Protein–Ligand Docking Structures

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Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields

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Improving Docking Power for Short Peptides Using Random Forest

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Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models

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Molecular Dynamics Fingerprints (MDFP): Machine Learning from MD Data To Predict Free-Energy Differences

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Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories

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K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

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Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

inferredcites →https://doi.org/10.1021/acs.jcim.9b00334openalex-snapshot

AGL-Score: Algebraic Graph Learning Score for Protein–Ligand Binding Scoring, Ranking, Docking, and Screening

inferredcites →https://doi.org/10.1021/acs.jcim.9b00387openalex-snapshot

Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation

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Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks

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Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field

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Learning Effective Molecular Models from Experimental Observables

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PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies

inferredcites →https://doi.org/10.1021/acs.jmedchem.9b01129openalex-snapshot

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

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Discovering a Transferable Charge Assignment Model Using Machine Learning

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Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

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OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction

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Extended-Connectivity Fingerprints

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NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes

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NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function

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Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis

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Structural Protein?Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study

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APIF: A New Interaction Fingerprint Based on Atom Pairs and Its Application to Virtual Screening

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PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical p K a Predictions

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Monte Carlo Free Ligand Diffusion with Markov State Model Analysis and Absolute Binding Free Energy Calculations

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Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

inferredcites →https://doi.org/10.1021/jm030331xopenalex-snapshot

Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding Interactions

inferredcites →https://doi.org/10.1021/jm0306430openalex-snapshot

Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy

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Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking

inferredcites →https://doi.org/10.1021/jp971990mopenalex-snapshot

Monte Carlo Simulations for Proteins: Binding Affinities for Trypsin−Benzamidine Complexes via Free-Energy Perturbations

inferredcites →https://doi.org/10.1038/nbt.1990openalex-snapshot

Comprehensive analysis of kinase inhibitor selectivity

inferredcites →https://doi.org/10.1038/s41467-018-06169-2openalex-snapshot

Towards exact molecular dynamics simulations with machine-learned force fields

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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

inferredcites →https://doi.org/10.1038/s41524-019-0261-5openalex-snapshot

Coarse-graining auto-encoders for molecular dynamics

inferredcites →https://doi.org/10.1038/s41570-020-0189-9openalex-snapshot

Exploring chemical compound space with quantum-based machine learning

inferredcites →https://doi.org/10.1038/s41586-021-03819-2openalex-snapshot

Highly accurate protein structure prediction with AlphaFold

inferredcites →https://doi.org/10.1038/s41597-022-01288-4openalex-snapshot

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

inferredcites →https://doi.org/10.1038/s41597-022-01390-7openalex-snapshot

QMugs, quantum mechanical properties of drug-like molecules

inferredcites →https://doi.org/10.1039/c8ob01406jopenalex-snapshot

Converting a weaker ATP-binding site inhibitor into a potent hetero-bivalent ligand by tethering to a unique peptide sequence derived from the same kinase

inferredcites →https://doi.org/10.1039/d0cs00098aopenalex-snapshot

QSAR without borders

inferredcites →https://doi.org/10.1039/d1sc05180fopenalex-snapshot

MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

inferredcites →https://doi.org/10.1039/d2sc02739aopenalex-snapshot

End-to-end differentiable construction of molecular mechanics force fields

inferredcites →https://doi.org/10.1063/5.0007276openalex-snapshot

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

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Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

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GraphDTA: predicting drug–target binding affinity with graph neural networks

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Kinase selectivity potential for inhibitors targeting the ATP binding site: a network analysis

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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

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Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

inferredcites →https://doi.org/10.1093/bioinformatics/bty593openalex-snapshot

DeepDTA: deep drug–target binding affinity prediction

inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot

ChEMBL: a large-scale bioactivity database for drug discovery

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The ChEMBL database in 2017

inferredcites →https://doi.org/10.1111/cbdd.14057openalex-snapshot

Machine learning approaches and their applications in drug discovery and design

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Correction to: The role of machine learning in clinical research: transforming the future of evidence generation

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Deep Learning Approaches in Predicting ADMET Properties

inferredcites →https://doi.org/10.48550/arxiv.1510.02855openalex-snapshot

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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