ArticleJournal of chemical information and modeling2021
Improving Docking Power for Short Peptides Using Random Forest.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Lipopeptide Engineering: From Natural Origins to Rational Design Against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2026Review
- PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.International journal of molecular sciences · 2026Article
- Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.International journal of molecular sciences · 2024Review
- How do physicochemical properties contribute to inhibitory activity of promising peptides against Zika Virus NS3 protease?Journal of molecular modeling · 2024Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study.Journal of pharmaceutical analysis · 2023Article
- [Progress on the design and optimization of antimicrobial peptides].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Recent PELE Developments and Applications in Drug Discovery Campaigns.International journal of molecular sciences · 2022Review
- Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.
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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.