CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs
  for Reduced hERG Liability
Gregory W. Kyro Matthew T. Martin Eric D. Watt Victor S. Batista
Drug-induced cardiotoxicity is a major health concern which can lead to
serious adverse effects including life-threatening cardiac arrhythmias via the
blockade of the voltage-gated hERG potassium ion channel. It is therefore of
tremendous interest to develop advanced methods to identify hERG-active
compounds in early stages of drug development, as well as to optimize
commercially available drugs for reduced hERG activity. In this work, we
present CardioGenAI, a machine learning-based framework for re-engineering both
developmental and marketed drugs for reduced hERG activity while preserving
their pharmacological activity. The framework incorporates novel
state-of-the-art discriminative models for predicting hERG channel activity, as
well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to
their potential implications in modulating the arrhythmogenic potential induced
by hERG channel blockade. These models can also serve independently as
effective components of a virtual screening pipeline. We applied the complete
framework to pimozide, an FDA-approved antipsychotic agent that demonstrates
high affinity to the hERG channel, and generated 100 refined candidates.
Remarkably, among the candidates is fluspirilene, a compound which is of the
same class of drugs (diphenylmethanes) as pimozide and therefore has similar
pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. We
have made all of our software open-source to facilitate integration of the
CardioGenAI framework for molecular hypothesis generation into drug discovery
workflows.