DECO: Decoupled Conditional Generation for Transcriptomics-Driven Drug Design

Transcriptomics-driven drug design seeks to generate molecules that induce desired cellular responses without requiring predefined molecular targets. A central challenge is that transcriptomic responses and molecular structures inhabit fundamentally different representation spaces, making it nontrivial to connect biological conditions to chemical generation. Existing approaches bridge these modalities through direct conditioning, cross-modal alignment, or coupled representation learning. Our analysis further reveals a weak and non-unique relationship between transcriptomic-response similarity and chemical similarity. Guided by this observation, we introduce DECO (DEcoupled COnditional generation), which learns chemical representations independently and models desired responses as conditional distributions over the resulting latent space. To make this decoupled space responsive to transcriptomic conditions, DECO combines global transition features for adaptive modulation with gene-level transition tokens for cross-attention. A generation-centered objective explicitly promotes condition dependence while retaining cross-modal alignment only as auxiliary semantic supervision. A prediction-error-aware decoder improves robustness to generator-induced latent deviations while preserving reconstruction fidelity. Across standard, cell-line OOD, and scaffold OOD splits, DECO achieves the best paired structural similarity and predicted transcriptomic consistency among evaluated methods, reducing Best@10 response MSE by 19.0–43.2% relative to the best-performing baseline in each split while maintaining 94.5–96.1% molecular validity.