Elucidating molecular mechanism and chemical space of chalcones through biological networks and machine learning approaches.

ABSTRACT: We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models (https://github.com/RatulChemoinformatics/QSAR) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the mo

SUBMITTER: Manaithiya A 

PROVIDER: S-EPMC11263914 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature