Title: DDI-Aware Domain Adaptation for Cross-Domain Drug Combination Representation Learning via Contrastive Embedding
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Domain adaptation, RKHS MMD alignment, Supervised Contrastive Learning

Abstract: Drug–drug interaction (DDI)–aware representation learning for combination therapy remains challenging under distribution shift: prevailing approaches tend to align marginals while neglecting the pairwise interaction structure that should be preserved across domains. We address these gaps with a DDI-aware, domain-adaptation–style framework that cleanly separates a population-level coupling objective from a tractable map-level surrogate. We present a conservative and fully specified framework integrating drug-drug interaction (DDI) structure into a domain-adaptation-style (DA-style) alignment view for association combination representation learning. Our composition has two purposely separated layers. In the paper coupling layer we add to classical optimal transport (OT) a structure-preserving DDI penalty that induces congruency between pairwise interaction structure between domains and we prove the existence of minimizers under standard measure theoretic conditions, without making any claims on determinism of the maps, called Monge maps. Under Dutch map layer and locally Lipschitz map layer and linear growth global well-posedness of induced preconditioned descent in map layer energy monotonicity is stated only under an explicit optional assumption of monotonicity of preconditioner not covering adaptive optimizers such as Adam. We present so-called Eulerian (continuity equation) view for help in interpretation but with clear indication of the scope of rigor. Empirically, we utilize the TWOSIDES proxy dataset to investigate whether DDI-aware pretraining will lead to better interaction-aware feature learning through the use of a proxy data-set (adverse-interaction). We emphasize that adverse DDI labels are not clinical non-synergy but conclusions are limited to the proxy discrimination task. On a variety of graph backbones and embedding baselines, supervised contrastive learning (SCL) with DA-style marginal alignment approaches leads to improvements in terms of Accuracy and Precision with competitive Recall.