Integrated Approach of Machine Learning and High-Throughput Screening to Identify Chemical Probe Candidates Targeting Aldehyde Dehydrogenases.

ABSTRACT: Selective chemical probes are essential for dissecting biological pathways and advancing drug discovery, yet developing high-quality probes for targets such as the aldehyde dehydrogenase (ALDH) family remains challenging. Here, we present a novel integrated approach combining experimental quantitative high-throughput screening (qHTS) with advanced machine learning (ML) and pharmacophore (PH4) modeling to rapidly identify selective inhibitors across multiple ALDH isoforms. We screened ∼13,000 annotated compounds against biochemical and cellular assays. We then utilized the data set to build ML and PH4 models to virtually screen a larger set of 174,000 compounds to enhance the chemical diversity of hits. This approach led to the expansion of chemically diverse isoform-selective inhibitors th

SUBMITTER: Yasgar A 

PROVIDER: S-EPMC12519302 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature