PAFDTA: Progressive Adaptive Fusion of Heterogeneous Representations with Cross-Branch Contrastive Alignment for Drug-Target Affinity Prediction

Drug-target affinity (DTA) prediction depends on learning representations that preserve complementary information from molecular structure, protein sequence, pair-specific interactions, and relational context. We propose a heterogeneous representation-learning framework, namely PAFDTA, combining graph-based molecular features, pretrained protein representations, drug-target pair context, and topology-aware relational priors. Multi-scale Local-Global Aggregation captures complementary molecular patterns, while Cross-Branch Contrastive Alignment uses relational context to regularize the molecular latent representation before fusion. Progressive Adaptive Fusion then coordinates the molecular, protein, and relational representations through pair-conditioned weighting and relation-level interaction modeling, while the joint drug-target representation is retained as a direct pair-context pathway for affinity regression. We evaluate PAFDTA on Davis and KIBA using five-fold warm evaluation, component ablations, and cold-start settings with unseen drugs and/or targets. Additional analyses examine prediction consistency, uncertainty-aware candidate prioritization, structural interpretation of learned protein representations, and a BRAF-oriented repositioning case. The results support heterogeneous representation learning and adaptive representation integration as effective principles for DTA prediction.