Synthon Contrastive Learning for Synthesizable 3D Molecule Generation

Nahyun Kim, Seul Lee, Sung Ju Hwang

Keywords: drug discovery, GFlowNet, contrastive learning

Abstract: To consider synthesizability in structure-based drug design (SBDD), a recent approach has proposed to co-design synthetic pathways with molecular conformers of ligands. However, the intermediate conformers are predicted based solely on the current intermediate molecule, without information about the synthons that will be added later along the synthetic pathway. As a result, the intermediate conformers do not adequately reflect the upcoming substructures, creating the geometric mismatch that leads to cumulative errors along the synthetic trajectory. To this end, we propose SYnthon Contrastive LEarning (SYCLE), a synthesizable SBDD framework that injects future synthon information into intermediate conformer prediction via contrastive learning. SYCLE further utilizes this information in the pathway generation policy to guide synthon selection. SYCLE achieves state-of-the-art binding affinity across all 15 LIT-PCBA targets. On CrossDocked2020, SYCLE attains -9.80 kcal/mol and an AiZynthFinder success rate of 68.8\%, nearly double that of the best baseline.

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Submission Number: 63