FIRM: From Interfaces to Realizations in Fragment-Based Molecular Generation

Individually valid fragments still require compatible attachments to form valid molecules. Yet compatible assembly alone does not ensure that generated molecules follow the reference distribution. This motivates learning connection requirements separately from fragment identities, allowing the model to select among compatible fragment structures according to the learned molecular distribution. We introduce FIRM, the Fragment Interface Realization Model, which generates typed connection requirements before selecting compatible fragment structures. FIRM uses two discrete flows. Interface Flow jointly models typed connections with a latent probabilistic circuit, while Conditional Realization Flow jointly refines fragment templates and attachment mappings through discrete denoising within the interface-defined compatible space. On MOSES and NPGen, conditioning on interfaces rather than untyped fragment topology reduces the empirical training-set conditional entropy of fragment templates and attachment mappings by approximately 61% and 49.5%, respectively. Generation-order comparisons show that interface-first generation improves success without structural correction by 3.25 and 2.67 percentage points over mixed-order generation. Additionally, FIRM more closely matches reference molecular feature distributions than the evaluated fragment-graph baselines. This formulation provides a foundation for controllable molecular design, allowing future methods to explore alternative fragment structures under specified connection requirements.