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

PubWeight™: 1.48‹?› | Rank: Top 4%

🔗 View Article (PMC 3524828)

Published in Bioinformatics on March 17, 2010

Authors

Pedro J Ballester1, John B O Mitchell

Author Affiliations

1: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. pedro.ballester@ebi.ac.uk

Articles citing this

Random forests for genomic data analysis. Genomics (2012) 1.98

Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J (2012) 1.68

Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol (2012) 1.41

istar: a web platform for large-scale protein-ligand docking. PLoS One (2014) 1.14

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification. J R Soc Interface (2012) 1.05

A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model (2011) 1.03

Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface (2012) 1.01

Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci Rep (2015) 0.99

Neural Networks for the Prediction of Organic Chemistry Reactions. ACS Cent Sci (2016) 0.92

Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model (2014) 0.92

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma. J Chem Inf Model (2016) 0.91

Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries. J Chem Inf Model (2011) 0.91

Using RosettaLigand for small molecule docking into comparative models. PLoS One (2012) 0.90

Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci (2014) 0.87

Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One (2013) 0.86

Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening. J Chem Inf Model (2013) 0.86

LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone. BMC Bioinformatics (2014) 0.85

Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J Chem Inf Model (2011) 0.85

Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation. J Comput Aided Mol Des (2013) 0.85

Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1). PLoS One (2012) 0.84

A functional feature analysis on diverse protein-protein interactions: application for the prediction of binding affinity. J Comput Aided Mol Des (2014) 0.84

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinformatics (2014) 0.84

One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery. J Chem Theory Comput (2013) 0.82

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip Rev Comput Mol Sci (2015) 0.81

Machine-learning techniques applied to antibacterial drug discovery. Chem Biol Drug Des (2015) 0.81

BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. BMC Bioinformatics (2015) 0.80

A D3R prospective evaluation of machine learning for protein-ligand scoring. J Comput Aided Mol Des (2016) 0.79

Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J Cheminform (2015) 0.79

A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach. J Comput Aided Mol Des (2014) 0.79

Receptor-ligand molecular docking. Biophys Rev (2013) 0.78

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins. BMC Bioinformatics (2015) 0.78

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations. J Am Chem Soc (2017) 0.77

Open source molecular modeling. J Mol Graph Model (2016) 0.77

Improved estimation of protein-ligand binding free energy by using the ligand-entropy and mobility of water molecules. Pharmaceuticals (Basel) (2013) 0.77

ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures. PLoS One (2016) 0.77

Correcting the impact of docking pose generation error on binding affinity prediction. BMC Bioinformatics (2016) 0.76

Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands. J Chem Inf Model (2015) 0.76

Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J Comput Chem (2016) 0.75

Novel small molecule binders of human N-glycanase 1, a key player in the endoplasmic reticulum associated degradation pathway. Bioorg Med Chem (2016) 0.75

Contact-based ligand-clustering approach for the identification of active compounds in virtual screening. Adv Appl Bioinform Chem (2012) 0.75

Potential Broad Spectrum Inhibitors of the Coronavirus 3CLpro: A Virtual Screening and Structure-Based Drug Design Study. Viruses (2015) 0.75

Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep (2017) 0.75

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge. Mol Inform (2015) 0.75

Protein-Ligand Scoring with Convolutional Neural Networks. J Chem Inf Model (2017) 0.75

Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors. ChemMedChem (2017) 0.75

Articles cited by this

The Protein Data Bank. Nucleic Acids Res (2000) 187.10

Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem (2004) 15.51

Development and validation of a genetic algorithm for flexible docking. J Mol Biol (1997) 14.36

Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem (2006) 8.75

Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov (2004) 8.10

Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci (2003) 5.93

Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des (1997) 5.71

Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des (2002) 5.65

The PDBbind database: methodologies and updates. J Med Chem (2005) 5.53

Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol (1995) 4.95

Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J Med Chem (2006) 3.85

Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J Comput Aided Mol Des (1996) 3.78

The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des (1994) 3.12

A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem (1999) 2.98

Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem (2003) 2.87

Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol (2000) 2.86

Assessing scoring functions for protein-ligand interactions. J Med Chem (2004) 2.41

Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol (1995) 2.23

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol (2007) 2.13

Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model (2009) 2.09

An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J Chem Inf Comput Sci (2004) 1.80

Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des (1998) 1.74

DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem (2005) 1.68

Community benchmarks for virtual screening. J Comput Aided Mol Des (2008) 1.49

Flexible docking using Tabu search and an empirical estimate of binding affinity. Proteins (1998) 1.45

LigScore: a novel scoring function for predicting binding affinities. J Mol Graph Model (2004) 1.38

Consideration of molecular weight during compound selection in virtual target-based database screening. J Chem Inf Comput Sci (2003) 1.37

Chemical probes that competitively and selectively inhibit Stat3 activation. PLoS One (2009) 1.37

Molecular mechanics methods for predicting protein-ligand binding. Phys Chem Chem Phys (2006) 1.34

y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model (2007) 1.33

Computational evaluation of protein-small molecule binding. Curr Opin Struct Biol (2009) 1.27

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases. J Mol Biol (2007) 1.24

General and targeted statistical potentials for protein-ligand interactions. Proteins (2005) 1.20

PMF scoring revisited. J Med Chem (2006) 1.12

Targeted scoring functions for virtual screening. Drug Discov Today (2009) 1.00

A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming. Proteins (2007) 1.00

A chemogenomic approach to drug discovery: focus on cardiovascular diseases. Drug Discov Today (2009) 0.99

Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods. J Chem Inf Comput Sci (2004) 0.94

Articles by these authors

Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics. J Chem Inf Model (2008) 1.69

L/D Protein Ligand Database (PLD): additional understanding of the nature and specificity of protein-ligand complexes. Bioinformatics (2003) 1.61

MACiE (Mechanism, Annotation and Classification in Enzymes): novel tools for searching catalytic mechanisms. Nucleic Acids Res (2006) 1.57

Understanding the functional roles of amino acid residues in enzyme catalysis. J Mol Biol (2009) 1.33

MACiE: a database of enzyme reaction mechanisms. Bioinformatics (2005) 1.32

Chemistry in bioinformatics. BMC Bioinformatics (2005) 1.18

Random forest models to predict aqueous solubility. J Chem Inf Model (2007) 1.15

In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt window. J Chem Inf Model (2013) 1.12

Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and Log P. J Chem Inf Model (2008) 1.06

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification. J R Soc Interface (2012) 1.05

Using reaction mechanism to measure enzyme similarity. J Mol Biol (2007) 1.04

Quantitative comparison of catalytic mechanisms and overall reactions in convergently evolved enzymes: implications for classification of enzyme function. PLoS Comput Biol (2010) 1.00

A structure-odour relationship study using EVA descriptors and hierarchical clustering. Org Biomol Chem (2004) 0.99

The chemistry of protein catalysis. J Mol Biol (2007) 0.98

A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem Cent J (2008) 0.97

Classifying molecules using a sparse probabilistic kernel binary classifier. J Chem Inf Model (2011) 0.96

Predicting phospholipidosis using machine learning. Mol Pharm (2010) 0.94

Chemoinformatics-based classification of prohibited substances employed for doping in sport. J Chem Inf Model (2006) 0.92

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des (2007) 0.92

Computational toxicology: an overview of the sources of data and of modelling methods. Expert Opin Drug Metab Toxicol (2009) 0.91

From sequence to enzyme mechanism using multi-label machine learning. BMC Bioinformatics (2014) 0.90

PFClust: a novel parameter free clustering algorithm. BMC Bioinformatics (2013) 0.90

Comments on "leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets": significance for the validation of scoring functions. J Chem Inf Model (2011) 0.89

Is EC class predictable from reaction mechanism? BMC Bioinformatics (2012) 0.89

Communication and re-use of chemical information in bioscience. BMC Bioinformatics (2005) 0.89

Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. J Chem Inf Model (2006) 0.87

Characterizing the complexity of enzymes on the basis of their mechanisms and structures with a bio-computational analysis. FEBS J (2011) 0.86

Predicting intrinsic aqueous solubility by a thermodynamic cycle. Mol Pharm (2008) 0.85

Predicting protein-ligand binding affinities: a low scoring game? Org Biomol Chem (2004) 0.83

The geometry of interactions between catalytic residues and their substrates. J Mol Biol (2007) 0.83

Scoring functions and enrichment: a case study on Hsp90. BMC Bioinformatics (2007) 0.82

Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases. Toxicol Appl Pharmacol (2008) 0.78

Is experimental data quality the limiting factor in predicting the aqueous solubility of druglike molecules? Mol Pharm (2014) 0.77

One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees. J Mol Evol (2014) 0.77

Predicting targets of compounds against neurological diseases using cheminformatic methodology. J Comput Aided Mol Des (2014) 0.76

Enzyme informatics. Curr Top Med Chem (2012) 0.76

Can we predict lattice energy from molecular structure? Acta Crystallogr B (2003) 0.76

How to winnow actives from inactives: introducing molecular orthogonal sparse bigrams (MOSBs) and multiclass Winnow. J Chem Inf Model (2008) 0.76

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules. J Chem Inf Model (2014) 0.75