ArticleJournal of chemical information and modeling2010
Binding affinity prediction with property-encoded shape distribution signatures.
Article in Journal of chemical information and modeling, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.Exploration of targeted anti-tumor therapy · 2023Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 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
- Molecular docking and structure-based drug design strategies.Molecules (Basel, Switzerland) · 2015Review
- Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?Journal of chemical information and modeling · 2014Article
- Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification.Journal of the Royal Society, Interface · 2012Article
- Robust scoring functions for protein-ligand interactions with quantum chemical charge models.Journal of chemical information and modeling · 2011Article
- Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries.Journal of chemical information and modeling · 2011Article
- Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.Wiley interdisciplinary reviews. Computational molecular scienceReview
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Authors and funding
3 authors.
Funding
Abstract
We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (DeltaH/-TDeltaS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach, and further improvement in accuracy would require accounting for these components rigorously.
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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.