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Characterization of Small Molecule Binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening

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What this work says

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

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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

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The particle concept: placing discrete water molecules during protein-ligand docking predictions

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Efficacy and selectivity in flexible database docking

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A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations

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Development and testing of a general amber force field

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Assessment of programs for ligand binding affinity prediction

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Predicting drug resistance of the HIV‐1 protease using molecular interaction energy components

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Characterization of PDZ domain‐peptide interaction interface based on energetic patterns

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Automated docking of substrates to proteins by simulated annealing

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A Fast Flexible Docking Method using an Incremental Construction Algorithm

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Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen

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Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4

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Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method

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A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease

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Characterization of Domain–Peptide Interaction Interface: A Case Study on the Amphiphysin-1 SH3 Domain

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Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation

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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

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Pairwise calculation of protein solvent-accessible surface areas

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ZINC − A Free Database of Commercially Available Compounds for Virtual Screening

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Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints

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Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

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Comparative Assessment of Scoring Functions on a Diverse Test Set

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

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Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening

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Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach

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Crystal structure of HIV-1 protease in complex with VX-478, a potent and orally bioavailable inhibitor of the enzyme

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HIV-1 Protease Inhibitors from Inverse Design in the Substrate Envelope Exhibit Subnanomolar Binding to Drug-Resistant Variants

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Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids

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

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

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Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening

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Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes

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Additivity in the Analysis and Design of HIV Protease Inhibitors

inferredcites →https://doi.org/10.1021/jp003919dopenalex-snapshot

Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides

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Characterization of Domain–Peptide Interaction Interface: Prediction of SH3 Domain-Mediated Protein–Protein Interaction Network in Yeast by Generic Structure-Based Models

inferredcites →https://doi.org/10.1023/a:1016357811882openalex-snapshot

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

inferredcites →https://doi.org/10.1038/254304a0openalex-snapshot

Structural invariants in protein folding

inferredcites →https://doi.org/10.1074/mcp.m800450-mcp200openalex-snapshot

Characterization of Domain-Peptide Interaction Interface

inferredcites →https://doi.org/10.1074/mcp.o111.010389openalex-snapshot

Proteome-wide Detection of Abl1 SH3-binding Peptides by Integrating Computational Prediction and Peptide Microarray

inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot

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

inferredcites →https://doi.org/10.1093/nar/gkl999openalex-snapshot

BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities

inferredcites →https://doi.org/10.1128/jvi.02531-09openalex-snapshot

Evaluating the Substrate-Envelope Hypothesis: Structural Analysis of Novel HIV-1 Protease Inhibitors Designed To Be Robust against Drug Resistance

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Open Babel: An open chemical toolbox

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In Silico and In Vitro: Identifying New Drugs

What points at this work

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

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Docking, Scoring, and Virtual Screening in Drug Discovery

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

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Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets

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

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From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

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Machine‐learning scoring functions for structure‐based virtual screening

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Featurization strategies for protein–ligand interactions and their applications in scoring function development

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Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity

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The Use of Random Forest to Predict Binding Affinity in Docking

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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

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An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking

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Modern Tools and Techniques in Computer-Aided Drug Design

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Can docking scoring functions guarantee success in virtual screening?

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Machine learning optimization of cross docking accuracy

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Comparative assessment of machine-learning scoring functions on PDBbind 2013

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Improved Prediction of Drug–Protein Interactions through Physics-Based Few-Shot Learning

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More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning

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Proteome-wide Detection of Abl1 SH3-binding Peptides by Integrating Computational Prediction and Peptide Microarray

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