CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.

ABSTRACT: The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activ

SUBMITTER: Kyro GW 

PROVIDER: S-EPMC11881490 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature