ArticleMolecular diversity2026
An innovative machine learning-based QSAR approach for prediction and structural analysis of novel/repurposed acid ceramidase (ASAH1) inhibitors for glioblastoma therapy.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights.Journal of computer-aided molecular design · 2026Article
- AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.Molecular diversity · 2026Article
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2 authors.
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Abstract
Acid ceramidase (ASAH1), a lysosomal enzyme that regulates ceramide and sphingosine-1-phosphate balance, has emerged as a promising therapeutic target in Glioblastoma. Inhibiting ASAH1 by carmofur elevates ceramide levels, inducing apoptosis in Glioblastoma cells. However, its clinical application is limited by its instability & toxicological concerns, thereby necessitating the search for more effective inhibitors. We employed an innovative machine learning-quantitative structure-activity relationship (ML-QSAR) approach to investigate & identify related bioactive ASAH1 inhibitors. Herein, we report the results of ML-QSAR modeling utilizing a filtered dataset of 103 inhibitors from ChEMBL & 431 3D descriptors. Multiple algorithmic steps, incorporating top five ML models, were implemented. Among these, our tuned extra trees regressor (ETR) model achieved the highest predictive performance (R
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