Synthesis-aware generative design in trillion-scale chemical spaces for automated drug discovery
Seo, S.; Sung, Y.; Hwang, S.-Y.; Kang, M.; Lee, J.; Bordi, S.; Raguz, L.; Choi, J.; Melnichenko, D.; Namkung, W.; Lee, S.; Lim, J.; Wanner, B. M.; Han, J. M.; Kim, W. Y.
Show abstract
Autonomous drug discovery via generative molecular design is critically bottlenecked by the production of chemically intractable structures. Multi-trillion-scale make-on-demand libraries guarantee synthetic feasibility, but conventional virtual screening cannot efficiently navigate these vast spaces or address multi-parameter optimization. We introduce Hyper Screening X, a generative AI framework integrating structure-based design with automated synthesis hardware. By encoding deterministic reaction logic in a generative flow network, the framework evaluates only 10 million candidates to navigate an 11-trillion-compound space, jointly optimizing physicochemical properties while ensuring compatibility with automated synthesis. Prospective validation against an SLC1A5 variant with a cryptic interface yielded a 96% synthesis success rate and a 50% functional hit rate, identifying two first-in-class lead compounds. This synthesis-aware strategy advances autonomous, closed-loop drug discovery.
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