MechMoE: Discovering Task-Aligned Perturbational Mechanisms for Out-of-Distribution Molecular Perturbation Prediction

Predicting transcriptional responses to molecular perturbations is a key challenge in computational drug discovery, particularly when generalizing to unseen compounds and cellular contexts. Although biological priors such as chemical similarity, annotated targets, and mechanism-of-action labels can provide useful structure, small-molecule responses often arise through indirect, context-dependent, and off-target effects that are only partially captured by predefined annotations. We introduce MechMoE, a mechanism-aware mixture model for out-of-distribution molecular perturbation prediction. MechMoE encodes molecular perturbations, basal cellular state, and expression-conditioned gene tokens, then uses learnable mechanism queries to extract candidate perturbational programs from contextualized gene representations. A sparse router selects a small subset of mechanism slots for each drug-cell pair, producing a perturbation-specific mechanism mixture used to predict the perturbation-induced expression change. We evaluate MechMoE on molecular perturbation data under strict held-out molecule and held-out cell-line settings. MechMoE improves transcriptome-wide response prediction across correlation, cosine similarity, reconstruction error, direction accuracy, and perturbation discrimination metrics, while remaining competitive on differential-expression recovery. Ablations show that sparse routing and the global perturbation decoder are both important for performance. These results suggest that small-molecule perturbation responses are effectively modeled as sparse combinations of learned, task-aligned latent mechanisms rather than fixed predefined biological categories.