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Development of a Rule-Based Method for the Assessment of Protein Druggability

https://doi.org/10.1021/ci200613b

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What this work says

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

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A simple method for displaying the hydropathic character of a protein

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Fragment screening to predict druggability (ligandability) and lead discovery success

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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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Drug-like Density: A Method of Quantifying the “Bindability” of a Protein Target Based on a Very Large Set of Pockets and Drug-like Ligands from the Protein Data Bank

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DrugPred: A Structure-Based Approach To Predict Protein Druggability Developed Using an Extensive Nonredundant Data Set

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Combining Global and Local Measures for Structure-Based Druggability Predictions

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Identifying and Characterizing Binding Sites and Assessing Druggability

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Drug- and Lead-likeness, Target Class, and Molecular Diversity Analysis of 7.9 Million Commercially Available Organic Compounds Provided by 29 Suppliers

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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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The PDBbind Database: Collection of Binding Affinities for Protein−Ligand Complexes with Known Three-Dimensional Structures

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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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Druggability Indices for Protein Targets Derived from NMR-Based Screening Data

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Understanding and Predicting Druggability. A High-Throughput Method for Detection of Drug Binding Sites

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Structure-based maximal affinity model predicts small-molecule druggability

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A Modified Kolmogorov-Smirnov Test Sensitive to Tail Alternatives

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A Structural View on Druggability

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Prediction, Analysis, and Comparison of Active Sites

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Protein–Ligand Blind Docking Using CB-Dock2

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Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives

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Impact of Protein Conformational Diversity on Structure-Based Prediction of Druggability

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