ArticlePLoS computational biology2023
Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Automated grading and diagnosis of sacroiliitis on CT images using a 3D convolutional neural network: a multicenter retrospective study.Scientific reports · 2026Article
- Tracking protein kinase targeting advances: integrating QSAR into machine learning for kinase-targeted drug discovery.Future science OA · 2025Review
- All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.Journal of chemical information and modeling · 2025Article
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool.Journal of cheminformatics · 2024Article
- Leveraging multiple data types for improved compound-kinase bioactivity prediction.Nature communications · 2024Article
- Revolutionizing Cancer Treatment: Unveiling New Frontiers by Targeting the (Un)Usual Suspects.Cancers · 2023Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model's root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.
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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.