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Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive

Mahdi Mousaei¹, Meruyert Kudaibergenova¹, Alexander D MacKerell²

¹Centre for Molecular Simulation, Department of Biological Sciences, University of Calgary, Calgary, Alberta T2N 1N4, Canada.

Journal of Chemical Information and Modeling

|November 16, 2020

Summary

Drug-induced cardiotoxicity from hERG1 channel blockade is a common drug side effect. A new SILCS/BML computational method accurately predicts drug blockers, aiding safer drug development.

Area of Science:

Computational Chemistry
Pharmacology
Biophysics

Background:

Drug-induced cardiotoxicity is a significant clinical concern, often linked to hERG1 potassium channel blockade.
Preclinical safety assessments for hERG1 blockade are costly and time-consuming.
Advances in cryo-EM have enabled molecular modeling for drug-target interactions.

Purpose of the Study:

To apply the Site Identification by Ligand Competitive Saturation (SILCS) protocol for mapping hERG1 channel hotspots.
To develop a computational method for rapid assessment of drug cardiotoxicity potential.
To salvage lead compounds by identifying key determinants of drug blockade.

Main Methods:

Blind application of the SILCS protocol using small solutes to sample chemical space.
Generation of FragMaps accounting for protein flexibility and interactions.
Augmentation with a Bayesian-optimization/machine-learning (BML) stage for weighting FragMap contributions.

Main Results:

SILCS/BML accurately predicted pIC50 values for 55 diverse hERG1 blockers (Pearson correlation > 0.535).
The SILCS/BML model significantly outperformed traditional rigid and flexible docking methods.
Accurate prediction requires proper weighting of drug protonation states at physiological pH.

Conclusions:

The SILCS/BML approach provides a rapid and effective in silico method for screening cardiotoxic potential.
This method can guide rational drug design, including the development of hERG1 activators.
Optimized SILCS FragMaps offer a valuable tool for preclinical drug safety assessment.

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