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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 The blockade ofthis ion channel can potentially lead fatal disorder and/or long QT syndrome.Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. Itis crucial to assess the hERG blockade activity in the early stage of drugdiscovery. We are particularly interested in the hERG-cardiotoxicity ofcompounds collected in the DrugBank database considering that many DrugBankcompounds have been approved for therapeutic treatments or have high potentialto become drugs. Machine learning-based in silico tools offer a rapid andeconomical platform to virtually screen DrugBank compounds. We design accurateand robust classifiers for blockers/non-blockers and then build regressors toquantitatively analyze the binding potency of the DrugBank compounds on thehERG channel. Molecular sequences are embedded with two natural languageprocessing (NPL) methods, namely, autoencoder and transformer. Complementarythree-dimensional (3D) molecular structures are embedded with two advancedmathematical approaches, i.e., topological Laplacians and algebraic graphs.With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBankcompounds are potential hERG blockers, suggesting serious drug safety problems.Our predictions provide guidance for the further experimental interrogation ofDrugBank compounds' hERG-cardiotoxicity .

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