ArticleJournal of chemical information and modeling2023
A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning.Journal of chemical information and modeling · 2026Article
- Informing development of brain cancer therapies within "preclinical trials" using ex vivo patient tumors.Advanced drug delivery reviews · 2026Review
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Machine Learning-Based Virtual Screening for the Identification of Novel CDK-9 Inhibitors.Biomolecules · 2025Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Machine learning-guided identification and simulation-based validation of potent JAK3 inhibitors for cancer therapy.PloS one · 2025Article
- Application of Artificial Intelligence-Based Approaches in the Discovery and Development of Protein Kinase Inhibitors (PKIs) Targeting Anticancer Activity.Current topics in medicinal chemistry · 2025Review
- Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance.bioRxiv : the preprint server for biology · 2024Article
- Leveraging multiple data types for improved compound-kinase bioactivity prediction.Nature communications · 2024Article
- BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many kinases remains only partly understood. In this study, we describe a computational approach to unlocking qualitative and quantitative kinome-wide binding measurements for structure-based machine learning. Our study has three components: (i) a Kinase Inhibitor Complex (KinCo) data set comprising
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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.