Abstract 5128: Accelerated discovery of novel RORγT modulators using an AI-driven platform integrating generative chemistry and mechanistic PK simulation

Abstract The application of artificial intelligence (AI) in oncology drug discovery offers the potential to significantly accelerate and de-risk the identification of novel therapeutic agents. In this study, we present the successful application of an AI-driven drug design (AIDD) platform to discover new classes of RORyT ligands, both agonists and inverse agonists. RORyT, a nuclear receptor central to Th17 cell differentiation and IL-17 signaling, has emerged as a promising target for auto-immune diseases as well as cancer immunology, and both classes of ligands have therapeutic potential. Our AIDD platform integrates quantitative structure-activity relationship (QSAR) modeling, ADMET property predictions, high-throughput mechanistic PK simulations, 3D volumetric/pharmacophore similarity scoring, and synthetic accessibility assessments at the point of initial compound design. Importantly, compound prioritization is guided by a multi-criteria decision analysis multi-parameter optimization (MPO) algorithm, which incorporates these features to balance potency, ADMET/PK, and chemical tractability to optimize candidate selection. This approach enables systematic decision-making across multiple parameters, reducing reliance on trial-and-error screening. In our initial Design/Make/Test cycle, 27 novel compounds were synthesized and tested. Remarkably, 70% demonstrated >25% inhibition of RORyT activity in cell-based assays. The top candidate had an IC50 of 1.51 uM and favorable in vitro ADME, with high concordance between predicted and measured values. Based on these results, a second set of inverse agonists was designed. 14/19 (74%) were active, with 4 having improved potency. In vitro and in vivo ADMET and PK testing is ongoing. In parallel work, we identified a novel class of RORyT agonists, which could find application in cancer immunotherapy; further development of this class is ongoing as well. This work demonstrates how a platform integrating generative chemistry with mechanistic PK simulations and MPO can be used to accelerate the development of viable drug candidates, in this case, against an important clinical target, RORyT Citation Format: Jeremy Jones, Rafal Bachorz, Michael Lawless, Joanna Pastwinska, Anna Salkowska, Marcin Ratajewski. Accelerated discovery of novel RORγT modulators using an AI-driven platform integrating generative chemistry and mechanistic PK simulation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5128.