A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors.

ABSTRACT: Background/Objectives: Human carbonic anhydrases (hCAs) are metalloenzymes involved in essential physiological processes, and their selective inhibition holds therapeutic potential across a wide range of disorders. However, the high degree of structural similarity among isoforms poses a significant challenge for the design of selective inhibitors. In this work, we present a machine learning (ML)-based platform for the isoform-specific prediction and profiling of small molecules targeting hCA I, II, IX, and XII. Methods: By integrating four molecular representations with four ML algorithms, we built 64 classification models, each extensively optimized and validated. The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compound

SUBMITTER: Piazza L 

PROVIDER: S-EPMC12300992 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature