ArticleJournal of chemical information and modeling2013
Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning.Journal of chemical information and modeling · 2026Article
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- 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
- Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.Journal of chemical theory and computation · 2020Article
- The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.Biomolecules · 2018Article
- Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.Frontiers in pharmacology · 2018Review
- Performance of machine-learning scoring functions in structure-based virtual screening.Scientific reports · 2017Article
- Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.Journal of computational chemistry · 2017Article
- Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery.Scientific reports · 2016Article
- MIEC-SVM: automated pipeline for protein peptide/ligand interaction prediction.Bioinformatics (Oxford, England) · 2016Article
- Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.Molecules (Basel, Switzerland) · 2015Article
- Structure-based virtual screening for drug discovery: principles, applications and recent advances.Current topics in medicinal chemistry · 2014Review
- Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.Wiley interdisciplinary reviews. Computational molecular scienceReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Accurately ranking docking poses remains a great challenge in computer-aided drug design. In this study, we present an integrated approach called MIEC-SVM that combines structure modeling and statistical learning to characterize protein-ligand binding based on the complex structure generated from docking. Using the HIV-1 protease as a model system, we showed that MIEC-SVM can successfully rank the docking poses and consistently outperformed the state-of-art scoring functions when the true positives only account for 1% or 0.5% of all the compounds under consideration. More excitingly, we found that MIEC-SVM can achieve a significant enrichment in virtual screening even when trained on a set of known inhibitors as small as 50, especially when enhanced by a model average approach. Given these features of MIEC-SVM, we believe it provides a powerful tool for searching for and designing new drugs.
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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.