Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models

TL;DR

This study employs topological Laplacian-assisted AI models to virtually screen DrugBank compounds for hERG channel blockers, identifying potential cardiotoxic drugs early in the drug discovery process.

Contribution

It introduces a novel combination of NLP and topological methods to accurately predict hERG blockade activity and potency of DrugBank compounds.

Findings

227 out of 8641 DrugBank compounds are potential hERG blockers

The models effectively classify blockers and non-blockers

Predictions highlight compounds with possible serious cardiotoxicity

Abstract

The human {\it ether-a-go-go} (hERG) potassium channel (K$_\text{v}11.1$) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NPL) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity .