GoMA-DTA: A Gene Ontology-Guided Multimodal Attention Fusion Model for Drug-Target Affinity Prediction

Accurate prediction of drug-target affinity (DTA) is essential for accelerating drug discovery. Although pretrained protein language models have achieved significant progress, existing methods predominantly focus on bottom-up sequence patterns and lack explicit constraints from high-level biological functions. We propose GoMA-DTA, a framework integrating gene ontology (GO) functional annotations with protein semantic features. GoMA-DTA introduces a channelwise gating mechanism that uses functional semantics as anchors to dynamically recalibrate ESM-2embeddings, achieving adaptive semantic filtering. For drugs, the model integrates Molformer-based semantic and TransConv-derived structural features. These dual-modality drug representations interact with calibrated protein features through a parallel synergistic architecture of cross-attention and Mamba modules, ensuring precise cross-modal alignment and efficient long-range dependency modeling. Evaluations on PDBBind, BindingDB, and ChEMBL benchmarks demonstrate that GoMA-DTA significantly outperforms state-of-the-art models across various evaluation scenarios. Its superior screening power is further validated on CASF-2016. Moreover, virtual screening of 200 000compounds against the SARS-CoV-2Spike protein, supported by experimental evidence (ZINC2111387), underscores its practical utility as a robust and biologically reliable tool. The datasets and codes are publicly available at https://github.com/xa-123955/GoMA-DTA.