GxEvo: Evolutionary Search for Transcriptome-Conditioned Molecular Design

Transcriptome-based drug design (TBDD) seeks molecules that induce a desired transcriptional response. This is a one-to-many inverse problem: a single desired response can correspond to multiple molecular scaffolds, motivating the generation of structurally diverse, response-aligned candidates. Existing TBDD generators, however, struggle to jointly achieve high structural similarity to known actives and scaffold novelty, and their promising outputs do not directly guide subsequent proposals. We introduce GxEvo, a population-based evolutionary framework that uses candidate evaluations to guide further generation under a fixed transcriptional objective. GxEvo combines a condition-specific seed generator, a forward response model trained with response prediction and contrastive learning for candidate discrimination, and a learned mutation operator conditioned on both the parent molecule and the target condition. Across ten gene-knockdown targets and three held-out drug–cell splits, GxEvo achieves the highest mean structural similarity to reference compounds among the evaluated methods. On knockdown targets, it generates 23.8 structurally hit-like unseen scaffolds absent from the task corpus per 100 valid molecules, compared with 3.9 for the strongest prior method under its standard sampling protocol. The advantage persists under matched candidate budgets, while controlled substitutions of existing response predictors and variation operators demonstrate the benefits of the proposed forward model and mutation operator. These results establish evolutionary search as an effective computational approach to generating novel, hit-like molecular hypotheses for subsequent experimental validation.