ArticleScientific reports2021
Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Machine learning-driven drug repurposing for HER2-positive breast cancer.Scientific reports · 2026Article
- Exploring pocket-aware inhibitors of BTK kinase by generative deep learning, molecular docking, and molecular dynamics simulations.RSC advances · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis.International journal of molecular sciences · 2025Article
- Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?Journal of chemical information and modeling · 2025Article
- Advances in pyrazolo[1,5-RSC advances · 2025Review
- DeepKinome: quantitative prediction of kinase binding affinity by a compound using deep learning based regression model.Frontiers in molecular biosciences · 2025Article
- Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.Journal of chemical information and modeling · 2024Article
- Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies.PNAS nexus · 2024Article
- Rationale,RSC advances · 2024Article
- A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.PLoS computational biology · 2024Article
- Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors.Journal of cheminformatics · 2024Article
- Pharmacology activity, toxicity, and clinical trials of Erythrina genus plants (Fabaceae): an evidence-based review.Frontiers in pharmacology · 2023Review
- Zebrafish as model system for the biological characterization of CK1 inhibitors.Frontiers in pharmacology · 2023Article
- The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors.Scientific reports · 2022Article
- KUALA: a machine learning-driven framework for kinase inhibitors repositioning.Scientific reports · 2022Article
- Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration.International journal of molecular sciences · 2022Article
- An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.Pharmaceutics · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.
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