Learning to Align Molecules and Proteins: A Geometry-Aware Approach to Binding Affinity

Accurate prediction of drug–target binding affinity can accelerate drug discovery by prioritizing promising compounds before costly wet‑lab screening. While deep learning has advanced this task, most models fuse ligand and protein representations via simple concatenation and lack explicit geometric regularization, resulting in poor generalization across chemical space and time. We introduce a lightweight framework that conditions molecular embeddings on protein embeddings through a feature‑wise linear modulation (FiLM) layer and enforces metric structure with a triplet loss. An RBF regression head operating on embedding distances yields smooth, interpretable affinity predictions. Despite its modest size, the proposed model achieves state‑of‑the‑art performance on the Therapeutics Data Commons DTI‑DG benchmark, as demonstrated by an extensive ablation study and out‑of‑domain evaluation. Our results underscore the value of conditioning and metric learning for robust drug–target affinity prediction.