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Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

https://doi.org/10.1021/ci500091r
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Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries
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SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes
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Comparative Assessment of Scoring Functions on a Diverse Test Set
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Mixed Quantum Mechanics/Molecular Mechanics Scoring Function To Predict Protein−Ligand Binding Affinity
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What points at this work

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

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Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation
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XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking
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Public Data Set of Protein–Ligand Dissociation Kinetic Constants for Quantitative Structure–Kinetics Relationship Studies
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SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction
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SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein–Ligand Interaction Modeling for METTL3
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Ligand–Receptor Affinities Computed by an Adapted Linear Interaction Model for Continuum Electrostatics and by Protein Conformational Averaging
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Beware of Machine Learning-Based Scoring Functions—On the Danger of Developing Black Boxes
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Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark
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Classification of Current Scoring Functions
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Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations
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Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning
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A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
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Modeling protein–ligand interactions for drug discovery in the era of deep learning
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TRPM8 virtual screening: the essential role of target-specific, inactive-enriched machine-learning scoring functions
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Computational analysis for residue-specific CDK2-inhibitor bindings
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Machine learning classification can reduce false positives in structure-based virtual screening
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In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor
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Exploring the binding behavior mechanism of vitamin B 12 to α-Casein and β-Casein: multi-spectroscopy and molecular dynamic approaches
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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction
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CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function
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Reducing false positive rate of docking-based virtual screening by active learning
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Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures
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Extended connectivity interaction features: improving binding affinity prediction through chemical description
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Robustly interrogating machine learning-based scoring functions: what are they learning?
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Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
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CSM-lig: a web server for assessing and comparing protein–small molecule affinities
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Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors
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GDEE: A Structure-Based Platform for Gene Discovery and Enzyme Engineering
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Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction
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IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
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Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
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Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors
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Rational Design of Small-Molecule Stabilizers of Spermine Synthase Dimer by Virtual Screening and Free Energy-Based Approach
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Computational methods for calculation of protein-ligand binding affinities in structure-based drug design
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Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function
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Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS
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Additional file 1 of SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors
inferred← citeshttps://doi.org/10.6084/m9.figshare.16822543openalex-snapshot
Additional file 1 of The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
inferred← citeshttps://doi.org/10.6084/m9.figshare.20098252openalex-snapshot
Additional file 1 of Explainable deep drug–target representations for binding affinity prediction
inferred← citeshttps://doi.org/10.6084/m9.figshare.20364828openalex-snapshot
Additional file 1 of Machine intelligence-driven framework for optimized hit selection in virtual screening
inferred← citeshttps://doi.org/10.6084/m9.figshare.20364831openalex-snapshot
Additional file 2 of Machine intelligence-driven framework for optimized hit selection in virtual screening
inferred← citeshttps://doi.org/10.6084/m9.figshare.20364834openalex-snapshot
Additional file 3 of Machine intelligence-driven framework for optimized hit selection in virtual screening
inferred← citeshttps://doi.org/10.6084/m9.figshare.20364837openalex-snapshot
Additional file 4 of Machine intelligence-driven framework for optimized hit selection in virtual screening
inferred← citeshttps://doi.org/10.6084/m9.figshare.20364840openalex-snapshot
Additional file 5 of Machine intelligence-driven framework for optimized hit selection in virtual screening
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Additional file 6 of Machine intelligence-driven framework for optimized hit selection in virtual screening
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Additional file 1 of Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors
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Computational design of synthesizable molecules by imitating reference chemistry
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Docking et Deep Learning : affinement de méthodologies in silico pour le développementde stratégies thérapeutiques innovantes
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Design and application of deep learning methods to structure-based drug design
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