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Multitask deep networks with grid featurization achieve improved scoring performance for protein–ligand binding

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What this work says

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

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Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO)

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AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

inferredcites →https://doi.org/10.1002/jcc.24667openalex-snapshot

Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest

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Improvements, trends, and new ideas in molecular docking: 2012–2013 in review

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Deep Learning in Drug Discovery

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Exploring protein native states and large‐scale conformational changes with a modified generalized born model

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Improved side‐chain torsion potentials for the Amber ff99SB protein force field

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Thermodynamic integration calculations of binding free energy difference for Gly‐169 mutation in subtilisin BPN′

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

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The rise of deep learning in drug discovery

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Automatic atom type and bond type perception in molecular mechanical calculations

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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

inferredcites →https://doi.org/10.1021/acs.chemrev.9b00055openalex-snapshot

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

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Boosting Docking-Based Virtual Screening with Deep Learning

inferredcites →https://doi.org/10.1021/acs.jcim.6b00740openalex-snapshot

Protein–Ligand Scoring with Convolutional Neural Networks

inferredcites →https://doi.org/10.1021/acs.jcim.7b00146openalex-snapshot

Is Multitask Deep Learning Practical for Pharma?

inferredcites →https://doi.org/10.1021/acs.jcim.7b00650openalex-snapshot

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

inferredcites →https://doi.org/10.1021/acs.jcim.8b00545openalex-snapshot

Comparative Assessment of Scoring Functions: The CASF-2016 Update

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Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations

inferredcites →https://doi.org/10.1021/ci200227uopenalex-snapshot

LigPlot+: Multiple Ligand–Protein Interaction Diagrams for Drug Discovery

inferredcites →https://doi.org/10.1021/ci300493wopenalex-snapshot

ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions

inferredcites →https://doi.org/10.1021/ci300604zopenalex-snapshot

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

inferredcites →https://doi.org/10.1021/ci500319fopenalex-snapshot

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

inferredcites →https://doi.org/10.1021/ci500731aopenalex-snapshot

Classification of Current Scoring Functions

inferredcites →https://doi.org/10.1021/ja512751qopenalex-snapshot

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

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Molecular Dynamics and Free Energy Studies on the Wild-type and Double Mutant HIV-1 Protease Complexed with Amprenavir and Two Amprenavir-Related Inhibitors: Mechanism for Binding and Drug Resistance

inferredcites →https://doi.org/10.1021/jm060999mopenalex-snapshot

Prediction of Protein−Ligand Interactions. Docking and Scoring: Successes and Gaps

inferredcites →https://doi.org/10.1021/jp404160yopenalex-snapshot

Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models

inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot

Deep learning

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Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark

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Applications of machine learning in drug discovery and development

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Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery

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Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set

inferredcites →https://doi.org/10.1039/c6cp01555gopenalex-snapshot

Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power

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MoleculeNet: a benchmark for molecular machine learning

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Comparison of simple potential functions for simulating liquid water

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Molecular dynamics with coupling to an external bath

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Particle mesh Ewald: An N ⋅log( N ) method for Ewald sums in large systems

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

inferredcites →https://doi.org/10.1093/bioinformatics/btu626openalex-snapshot

PDB-wide collection of binding data: current status of the PDBbind database

inferredcites →https://doi.org/10.1111/cbdd.12051openalex-snapshot

Accounting for Receptor Flexibility and Enhanced Sampling Methods in Computer‐Aided Drug Design

inferredcites →https://doi.org/10.3389/fmolb.2017.00087openalex-snapshot

Recent Developments and Applications of the MMPBSA Method

inferredcites →https://doi.org/10.48550/arxiv.1502.02072openalex-snapshot

Massively Multitask Networks for Drug Discovery

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

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

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Molecular substructure tree generative model for de novo drug design

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Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning

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Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm

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Design and application of deep learning methods to structure-based drug design

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