ArticleBriefings in bioinformatics2022
Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed, 127 citations in OpenAlex.
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- Drug Repurposing to Inhibit Oncostatin M in Crohn's Disease.Molecules (Basel, Switzerland) · 2025Article
- A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction.Communications chemistry · 2025Article
- iScore: A ML-Based Scoring Function for De Novo Drug Discovery.Journal of chemical information and modeling · 2025Article
- LeScore: a scoring function incorporating hydrogen bonding penalty for protein-ligand docking.Journal of molecular modeling · 2025Article
- Virtual screening: hope, hype, and the fine line in between.Expert opinion on drug discovery · 2025Review
- Anticancer activity ofHeliyon · 2025Article
- Target fishing: from "needle in haystack" to "precise guidance"--new technology, new strategy and new opportunity.Frontiers in pharmacology · 2025Review
- New insights into the evolution and function of the UMAMIT (USUALLY MULTIPLE ACIDS MOVE IN AND OUT TRANSPORTER) gene family.Journal of plant research · 2025Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein-ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.
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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.