DEG2MOL: Conditional Latent Flow Matching for Transcriptome-Guided de novo Drug Design

Abstract

Transcriptomic profiles capture cellular responses to perturbations, offering a principled foundation for biologically grounded de novo drug design. However, existing transcriptome-guided generative approaches remain limited in biological specificity, transcriptomic coverage, and evaluation across diverse perturbation contexts. Here, we propose DEG2MOL, a biologically grounded conditioning framework that encodes differentially expressed gene profiles through a Gene Ontology (GO)-informed encoder and maps this representation into the latent space of a frozen, pre-trained scaffold-aware VAE via conditional latent flow matching. When benchmarked against five baseline models, DEG2MOL ranked first by rank-sum aggregation of six metrics in both the random- and scaffold-split evaluations. For two representative compound conditions, GO-informed encoder activations were accompanied by enrichment of the corresponding target-engaging pharmacophores among the generated molecules. In docking case studies of two compound–target pairs, generated molecules showed favorable predicted docking scores and reference-associated contacts. DEG2MOL also generated molecules from shRNA knockdown, CRISPR knockout, and Perturb-seq profiles without additional training, indicating transfer across the perturbation types and profiling platforms evaluated. At the transcriptional level, an in silico gene-expression analysis showed that the predicted signatures of generated molecules were more similar to those of target-matched inhibitors than to those of unrelated inhibitors. The data and code are available at https://github.com/KU-MedAI/DEG2MOL.