ArticleScientific reports2024
Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep learning model for activity cliffs prediction: a comprehensive approach to protein kinase inhibitors.Journal of computer-aided molecular design · 2025Article
- Efficiently finding activity cliffs.bioRxiv : the preprint server for biology · 2025Article
- What is the future role of quantum computation in drug discovery?Emerging topics in life sciences · 2025Review
- Prediction of enzyme inhibition (ICJournal of molecular modeling · 2025Article
- Scaffold and SAR studies on c-MET inhibitors using machine learning approaches.Journal of pharmaceutical analysis · 2025Article
- In Silico Identification of 2,4-Diaminopyrimidine-Based Compounds as Potential CK1ε Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
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Authors and funding
2 authors.
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Abstract
Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.
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