ArticleNature computational science2026
TANGO: direct optimization of constrained synthesizability for generative molecular design.
Article in Nature computational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Constrained synthesizability is an unaddressed challenge in generative molecular design: in particular, designing molecules satisfying multi-parameter optimization objectives, while simultaneously being synthesizable and enforcing the presence of specific building blocks in the synthesis. This is practically important for molecule re-purposing, sustainability and efficiency. Here we propose the Tanimoto Group Overlap (TANGO) reward function, which uses chemistry principles to transform a binary reward function into a continuous reward function. TANGO can augment molecular generative models to directly optimize for constrained synthesizability using reinforcement learning (RL). Our framework is general and addresses starting-material, intermediate and divergent-synthesis constraints. Contrary to many existing works in the field, we show that incentivizing a general-purpose model with RL is a productive approach to navigating challenging synthesizability optimization scenarios.
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