Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.

ABSTRACT: Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs in silico, optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity. We used these models to virtually screen a vendor library of 5 million compounds for BIs and tested 20 of these compounds in vitro. Seven of the 20 compounds displayed selectivi

SUBMITTER: Ozalp MK 

PROVIDER: S-EPMC11331448 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature