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...
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| Language: | English |
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BMC
2025-03-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00976-8 |
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| author | Gregory W. Kyro Matthew T. Martin Eric D. Watt Victor S. Batista |
| author_facet | Gregory W. Kyro Matthew T. Martin Eric D. Watt Victor S. Batista |
| author_sort | Gregory W. Kyro |
| collection | DOAJ |
| description | 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 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. 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 as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, NaV1.5 and CaV1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source at https://github.com/gregory-kyro/CardioGenAI to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows. Scientific contribution This work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, NaV1.5 and CaV1.2 channel activity, which can function independently as effective components of virtual screening pipelines. |
| format | Article |
| id | doaj-art-e7a6b30f5289491097ca8da9a8f58e8a |
| institution | OA Journals |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-e7a6b30f5289491097ca8da9a8f58e8a2025-08-20T01:57:49ZengBMCJournal of Cheminformatics1758-29462025-03-0117112010.1186/s13321-025-00976-8CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liabilityGregory W. Kyro0Matthew T. Martin1Eric D. Watt2Victor S. Batista3Department of Chemistry, Yale UniversityDrug Safety Research & Development, Pfizer Research & DevelopmentDrug Safety Research & Development, Pfizer Research & DevelopmentDepartment of Chemistry, Yale UniversityAbstract 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 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. 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 as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, NaV1.5 and CaV1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source at https://github.com/gregory-kyro/CardioGenAI to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows. Scientific contribution This work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, NaV1.5 and CaV1.2 channel activity, which can function independently as effective components of virtual screening pipelines.https://doi.org/10.1186/s13321-025-00976-8Generative AIDeep learningMolecular optimizationhERGDrug discoveryMachine learning |
| spellingShingle | Gregory W. Kyro Matthew T. Martin Eric D. Watt Victor S. Batista CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability Journal of Cheminformatics Generative AI Deep learning Molecular optimization hERG Drug discovery Machine learning |
| title | CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability |
| title_full | CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability |
| title_fullStr | CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability |
| title_full_unstemmed | CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability |
| title_short | CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability |
| title_sort | cardiogenai a machine learning based framework for re engineering drugs for reduced herg liability |
| topic | Generative AI Deep learning Molecular optimization hERG Drug discovery Machine learning |
| url | https://doi.org/10.1186/s13321-025-00976-8 |
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