Abstract 979: DrugVLAB for oncogenic kinase inhibitor generation

Abstract Introduction: The dysregulation of kinases is a major mechanism for cancer development and progression. Rapid advances in generative deep learning technologies now allow models to generate candidate kinase inhibitors. However, with over 500 human kinases, the major challenge is to design inhibitors that are highly selective for the oncogenic kinase without blocking other essential ones. Thus, all generated molecules need to be tested rigorously for interactions with specific residues and overall affinity to pockets of both target and off-target kinases. This task requires orchestrating multiple AI tools in a well-defined workflow, which is not trivial to researchers. Methods: To address the challenges, we developed DrugVLAB for Kinase Inhibitor Generation, a comprehensive Amazon cloud-based workflow, built through our collaboration, that enables human-in-the-loop search. Workflow: The workflow consists of more than 20 cutting edge AI tools. It begins with in-house fragment-based molecule generations (ICLR 2025). Users can specify requirements such as residue-atom interactions and drug-likeness filters using in-house tools (JCIM 2025, ICML 2025, ISMB 2025). Candidate molecules then undergo docking simulations with Autodock Vina, followed by additional filtering based on residue-atom interactions while considering docking pose. Finally, drug target affinity (DTA) can be rigorously checked using in-house tools (ICLR 2025, ISMB 2025). At this stage, molecules are ranked by DTA values for synthesis and evaluation. As molecules are generated with fragments, most of them are synthesizable. This will conclude the execution of one round of DrugVLABTM for Kinase. A unique and notable feature of our cloud system is to incorporate assay results of newly synthesized and evaluated molecules. Our system identifies fragments or substructures enriched in active and inactive molecules. With these new fragment sets, itinitiates the next round of assay-guided molecule generation. Our experience is that better, more active molecules are generated as the round goes on. Results: DrugVLAB can generate 3000 molecules on Amazon cloud in 2.5 hours and a complete round of evaluation can be done in 2.5 hours for a target kinase and five off-targets. Conclusion: Our system is implemented on Amazon cloud, enabling researchers around the world to generate and evaluate molecules as kinase inhibitors. DrugVLAB for kinase is designed in a modular way so that any newly developed AI tools can be incorporated easily and timely. Citation Format: Sun Kim, Bokyung hyerin kim, Park, Joonho Seong, Seokchol kim youngkuk, Hong, Changyun Cho, Heejoon Chae, Kyoung Jae Won. DrugVLAB for oncogenic kinase inhibitor generation [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 979.