CoCoBind: Consistency-Contrastive Multitask Learning for RNA–Ligand Interaction and Binding Site Prediction
RNA-targeted small-molecule therapeutics are of growing clinical interest, but computational modeling of RNA–ligand recognition remains challenging because of limited curated data, conformational heterogeneity, and the close coupling between interaction prediction and binding-site localization. We present CoCoBind, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture. CoCoBind integrates pretrained RNA and molecular representation models with cross-modal cross-attention, a Noisy-OR-based consistency constraint, and contrastive alignment. On the DeepRNA-DTI benchmark, CoCoBind shows its clearest interaction-prediction gain in the most stringent Unseen Both setting and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distribution shift. Structure-level analyses further support the localization of ligand-proximal RNA pocket neighborhoods. CoCoBind thus provides a site-aware computational framework for RNA–ligand interaction modeling and AI-enabled hit prioritization in RNA-targeted small-molecule discovery.