(Q31119539)

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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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scholarly article

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Europe PubMed Central

PubMed publication ID

29 June 2017

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

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Europe PubMed Central

PubMed publication ID

29 June 2017

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Pedro J. Ballester

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Pedro J Ballester

4

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author name string

Hongjian Li

1

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Europe PubMed Central

PubMed publication ID

29 June 2017

Kwong-Sak Leung

2

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Europe PubMed Central

PubMed publication ID

29 June 2017

Man-Hon Wong

3

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Europe PubMed Central

PubMed publication ID

29 June 2017

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12 February 2015

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Europe PubMed Central

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29 June 2017

Molecular Informatics

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Europe PubMed Central

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29 June 2017

34

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Europe PubMed Central

PubMed publication ID

29 June 2017

2-3

1 reference

Europe PubMed Central

PubMed publication ID

29 June 2017

115-126

1 reference

Europe PubMed Central

PubMed publication ID

29 June 2017

Efficient drug lead discovery and optimization

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21 January 2018

Virtual screening: an endless staircase?

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Structure-based virtual screening for drug discovery: a problem-centric review

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The holistic integration of virtual screening in drug discovery

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Structure-Based Approaches to Target Fishing and Ligand Profiling

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Novel computational approaches to polypharmacology as a means to define responses to individual drugs

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Drug repositioning by structure-based virtual screening

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Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

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DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment

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21 January 2018

Bioinformatics and variability in drug response: a protein structural perspective

1 reference

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Structure-based activity prediction for an enzyme of unknown function

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Structure-based target druggability assessment.

1 reference

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Automated docking with grid-based energy evaluation

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Molecular mechanics methods for predicting protein-ligand binding.

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The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure

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21 January 2018

Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.

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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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Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming

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A general and fast scoring function for protein-ligand interactions: a simplified potential approach.

1 reference

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General and targeted statistical potentials for protein-ligand interactions.

1 reference

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Knowledge-based scoring function to predict protein-ligand interactions.

1 reference

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Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

1 reference

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

1 reference

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Binding affinity prediction with property-encoded shape distribution signatures

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NNScore 2.0: a neural-network receptor-ligand scoring function

1 reference

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Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries

1 reference

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A machine learning-based method to improve docking scoring functions and its application to drug repurposing

1 reference

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21 January 2018

Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?

1 reference

https://api.crossref.org/works/10.1002%2FMINF.201400132

21 January 2018

Additivity principles in biochemistry

1 reference

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21 January 2018

Non-additivity of functional group contributions in protein-ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry

1 reference

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21 January 2018

Latest developments in molecular docking: 2010-2011 in review

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21 January 2018

Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model

1 reference

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21 January 2018

ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions

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Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

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istar: a web platform for large-scale protein-ligand docking

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21 January 2018

Integrating docking scores, interaction profiles and molecular descriptors to improve the accuracy of molecular docking: toward the discovery of novel Akt1 inhibitors

1 reference

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Scoring functions for prediction of protein-ligand interactions

1 reference

https://api.crossref.org/works/10.1002%2FMINF.201400132

21 January 2018

Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets.

1 reference

https://api.crossref.org/works/10.1002%2FMINF.201400132

21 January 2018

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.

1 reference

https://api.crossref.org/works/10.1002%2FMINF.201400132

21 January 2018

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

1 reference

https://api.crossref.org/works/10.1002%2FMINF.201400132

21 January 2018

Robust scoring functions for protein-ligand interactions with quantum chemical charge models

1 reference

https://api.crossref.org/works/10.1002%2FMINF.201400132

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Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests

1 reference

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Prediction of protein-ligand interactions. Docking and scoring: successes and gaps

1 reference

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Comparison of topological, shape, and docking methods in virtual screening.

1 reference

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Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes

1 reference

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Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database.

1 reference

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Comparative assessment of scoring functions on a diverse test set.

1 reference

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

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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

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

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Statistical potential for modeling and ranking of protein-ligand interactions

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A consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes: methods behind the HYDE scoring function

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PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements

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HotLig: a molecular surface-directed approach to scoring protein-ligand interactions

1 reference

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Prediction of protein-ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations

1 reference

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aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction.

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Comparing neural-network scoring functions and the state of the art: applications to common library screening

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Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening

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BINANA: a novel algorithm for ligand-binding characterization

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PostDOCK: a structural, empirical approach to scoring protein ligand complexes.

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NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes

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DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes

1 reference

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8 April 2023

1 reference

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Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities

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LigScore: a novel scoring function for predicting binding affinities

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

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CSAR scoring challenge reveals the need for new concepts in estimating protein-ligand binding affinity

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8 April 2023

Identifiers

10.1002/MINF.201400132

1 reference

Europe PubMed Central

PubMed publication ID

29 June 2017

PubMed publication ID

1 reference

Europe PubMed Central

PubMed publication ID

29 June 2017

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