The properties of a new drug are set at the point of design, including any potential design flaws. Many small molecules that make it to human trials fail to progress beyond Phase 1 due to these flaws, which manifest as unacceptable side effects (either alone or in combination), meaning the drug (and therefore the pathway it is modulating) can never be adequately evaluated. This presentation will highlight how, through the thoughtful generation of a precise target candidate profile (TCP) and the subsequent use of GenAI, clinical candidates can now be precision designed to more exacting standards. Such molecules have the potential to have improved therapeutic indices, even against protein targets that are usually associated with mechanism-based adverse events. Citation Format: David J. Hallett. Overcoming traditional design limitations with AI-based discovery. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA004
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