ArticleJournal of cheminformatics2024
Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
- Next-generation Janus kinase inhibitors: Integrating synthetic innovation, structural biology, and computational design for precision drug discovery.Pharmaceutical science advances · 2026Review
- KLSD: A Curated Kinase-Ligand Database Mapping Selectivity Landscapes and Polypharmacology.ACS omega · 2026Article
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- Comparing massively-multitask regression algorithms for drug discovery.Journal of computer-aided molecular design · 2026Article
- Transverse Comparison of Clinical Characteristics and Prognostic Factors in HIV-Infected Patients with Cytomegalovirus Encephalitis in Guangdong, China.Current HIV research · 2026Article
- PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.Frontiers in pharmacology · 2026Article
- Machine learning guided virtual screening of FDA approved drugs targeting GSK-3β in Alzheimer's disease.Scientific reports · 2025Article
- Establishment of an Integrated Model for Predicting Compound Mutagenicity with a Feature Importance Analysis.Journal of chemical information and modeling · 2025Article
- Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.Journal of computer-aided molecular design · 2025Article
- Predicting cardiotoxicity in drug development: A deep learning approach.Journal of pharmaceutical analysis · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Kinase-inhibitor binding affinity prediction with pretrained graph encoder and language model.Briefings in bioinformatics · 2025Article
- Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.Diabetologia · 2025Review
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Frontiers in pharmacology · 2025Article
- Investigating the anti-obesity potential ofFrontiers in pharmacology · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.
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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.