ArticleProteins2020
Using machine learning to improve ensemble docking for drug discovery.
Article in Proteins, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Multicomponent synthesis of novel imidazole-pyran derivatives:RSC advances · 2025Article
- Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery.iScience · 2024Article
- Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity.Molecules (Basel, Switzerland) · 2024Article
- Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma.Acta pharmacologica Sinica · 2024Article
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor.Frontiers in microbiology · 2023Article
- Protein-Ligand Docking in the Machine-Learning Era.Molecules (Basel, Switzerland) · 2022Review
- AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens.Journal of chemical information and modeling · 2022Article
- Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery.Frontiers in molecular biosciences · 2022Article
- Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.Journal of chemical information and modeling · 2021Article
- EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery.Protein science : a publication of the Protein Society · 2021Article
- Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2021Article
- Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia.Risk management and healthcare policy · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activity, researchers have not yet come up with effective ways to use these scores to classify compounds into actives and inactives. This shortcoming has led to the decrease, rather than an increase in the performance of classifying compounds when more structures are added to the ensemble. Previously, we suggested machine learning, implemented in the form of a naïve Bayesian model could alleviate this problem. However, the naïve Bayesian model assumed that the probabilities of observing the docking scores to different structures to be independent. This approximation might prevent it from achieving even higher performance. In the work presented in this paper, we have relaxed this approximation when using several other machine learning methods-k nearest neighbor, logistic regression, support vector machine, and random forest-to improve ensemble docking. We found significant improvement.
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Registered trials
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