ArticleACS omega2022
Machine-Learning- and Knowledge-Based Scoring Functions Incorporating Ligand and Protein Fingerprints.
Article in ACS omega, 2022. 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.
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- Molegro Data Modeller to Estimate CDK6 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Neural Networks to Calculate CDK2 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- CDK7 as a Target for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning to Predict CDK4 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AlphaFold for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Targeting CDK9 with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Differential Evolution for Docking Simulations.Methods in molecular biology (Clifton, N.J.) · 2026Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease.Journal of molecular structure · 2023Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a novel machine-learning-based scoring function for drug discovery that incorporates ligand and protein structural information into a knowledge-based PMF score. Molecular docking, a simulation method for structure-based drug design (SBDD), is expected to reduce the enormous costs associated with conventional experimental methods in terms of rational drug discovery. Molecular docking has two main purposes: to predict ligand-binding structures for target proteins and to predict protein-ligand binding affinity. Currently available programs of molecular docking offer an accurate prediction of ligand binding structures for many systems. However, the accurate prediction of binding affinity remains challenging. In this study, we developed a new scoring function that incorporates fingerprints representing ligand and protein structures as descriptors in the PMF score. Here, regression analysis of the scoring function was performed using the following machine learning techniques: least absolute shrinkage and selection operator (LASSO) and light gradient boosting machine (LightGBM). The results on a test data set showed that the binding affinity delivered by the newly developed scoring function has a Pearson correlation coefficient of 0.79 with the experimental value, which surpasses that of the conventional scoring functions. Further analysis provided a chemical understanding of the descriptors that contributed significantly to the improvement in prediction accuracy. Our approach and findings are useful for rational drug discovery.
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Registered trials
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