ArticleJournal of computer-aided molecular design2025
Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.
Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Baicalin attenuates angiotensin II-induced vascular endothelial injury via targeted activation of the Nrf2 signaling pathway: Integrated computational and in vitro evidence.Molecular diversity · 2026Article
- Discovery of novel InhA inhibitors through structural bioinformatics and machine learning-driven QSAR screening of natural products.Biology methods & protocols · 2026Article
- 2-Hydroxyglutarate in onco-pathway: a metabolic driver of proto-oncogene activation.Frontiers in oncology · 2026Review
- In Silico Identification of the NLRP3 Inhibitors from Traditional Chinese Medicine.International journal of molecular sciences · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors that could target mIDH1, which may provide more effective treatment options for patients with related tumors. In present study, combines machine learning-based QSAR models and structure-based virtual screening to screen a series of potential IDH1 inhibitors from the Coconut databases. The QSAR model predictions indicate that the hit compounds have high binding affinity to the target protein, and its pIC
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