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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 Na\n                    <jats:sub>V<\/jats:sub>\n                    1.5 and Ca\n                    <jats:sub>V<\/jats:sub>\n                    1.2 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, Na\n                    <jats:sub>V<\/jats:sub>\n                    1.5 and Ca\n                    <jats:sub>V<\/jats:sub>\n                    1.2 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\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/gregory-kyro\/CardioGenAI\" ext-link-type=\"uri\">https:\/\/github.com\/gregory-kyro\/CardioGenAI<\/jats:ext-link>\n                    to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Scientific contribution<\/jats:bold>\n                  <\/jats:p>\n                  <jats:p>\n                    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, Na\n                    <jats:sub>V<\/jats:sub>\n                    1.5 and Ca\n                    <jats:sub>V<\/jats:sub>\n                    1.2 channel activity, which can function independently as effective components of virtual screening pipelines.\n                  <\/jats:p>","DOI":"10.1186\/s13321-025-00976-8","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T06:02:36Z","timestamp":1741154556000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability"],"prefix":"10.1186","volume":"17","author":[{"given":"Gregory W.","family":"Kyro","sequence":"first","affiliation":[]},{"given":"Matthew T.","family":"Martin","sequence":"additional","affiliation":[]},{"given":"Eric D.","family":"Watt","sequence":"additional","affiliation":[]},{"given":"Victor S.","family":"Batista","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"976_CR1","unstructured":"Food; Administration, D.; Health, U. 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