ArticleJournal of chemical information and modeling2011
Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries.
Article in Journal of chemical information and modeling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
34 citing papers in PubMed, 104 citations in OpenAlex.
- Distance plus attention for binding affinity prediction.Journal of cheminformatics · 2024Article
- Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure-Activity Relationships.ACS omega · 2024Article
- Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.PloS one · 2024Article
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction.PLoS computational biology · 2022Article
- Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.Scientific reports · 2022Article
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
- Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.Journal of computer-aided molecular design · 2021Article
- Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.Briefings in bioinformatics · 2021Article
- Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.Molecular diversity · 2021Article
- GNINA 1.0: molecular docking with deep learning.Journal of cheminformatics · 2021Article
- QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network.RSC advances · 2020Article
- Machine learning classification can reduce false positives in structure-based virtual screening.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data.ACS chemical biology · 2020Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- Structure-Based Virtual Screening: From Classical to Artificial Intelligence.Frontiers in chemistry · 2020Review
- Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019Review
- The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.Biomolecules · 2018Article
- Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking.ACS omega · 2017Article
- Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors.ChemMedChem · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The community structure-activity resource (CSAR) data sets are used to develop and test a support vector machine-based scoring function in regression mode (SVR). Two scoring functions (SVR-KB and SVR-EP) are derived with the objective of reproducing the trend of the experimental binding affinities provided within the two CSAR data sets. The features used to train SVR-KB are knowledge-based pairwise potentials, while SVR-EP is based on physicochemical properties. SVR-KB and SVR-EP were compared to seven other widely used scoring functions, including Glide, X-score, GoldScore, ChemScore, Vina, Dock, and PMF. Results showed that SVR-KB trained with features obtained from three-dimensional complexes of the PDBbind data set outperformed all other scoring functions, including best performing X-score, by nearly 0.1 using three correlation coefficients, namely Pearson, Spearman, and Kendall. It was interesting that higher performance in rank ordering did not translate into greater enrichment in virtual screening assessed using the 40 targets of the Directory of Useful Decoys (DUD). To remedy this situation, a variant of SVR-KB (SVR-KBD) was developed by following a target-specific tailoring strategy that we had previously employed to derive SVM-SP. SVR-KBD showed a much higher enrichment, outperforming all other scoring functions tested, and was comparable in performance to our previously derived scoring function SVM-SP.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.