PhenTTP: Phenotype-Driven Molecular Generation with Transferable Target Priors

Drug discovery ultimately seeks molecules that elicit desired biological responses. Phenotype-driven generation pursues this functional objective directly from cellular responses without requiring a predefined target. However, the inverse mapping from downstream cellular response to molecular structure is underdetermined. To reduce this ambiguity, we propose PhenTTP, a phenotype-driven molecular generation framework with transferable target priors. PhenTTP uses a state-transition phenotype encoder and proceeds in three stages: it aligns phenotype-derived representations with target-conditioned priors, learns phenotype-conditioned generation from phenotype-molecule pairs, and transfers target-conditioned behavior through dual-route distillation, supporting both phenotype-only and phenotype-plus-target generation. Across chemical, scaffold, cell-line, and target out-of-distribution settings, the phenotype-only route lowers Vina scores by 0.13-0.54 kcal/mol over the best phenotype-only baseline despite receiving no targets at inference; when a target is available, the target-informed route further increases Boltz-2 binder probability by 6.6-18.3 percentage points over the same baselines while maintaining phenotype correlation similar to that of the phenotype-only route and achieving high molecular validity, uniqueness, and novelty. Together, these results show that PhenTTP improves target compatibility by transferring target-conditioned molecular priors while preserving phenotype fidelity, establishing a framework that links cellular functional objectives to molecular-level constraints in generative drug discovery.