LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.

Identifying protein-ligand binding residues is fundamental to unlocking molecular recognition and advancing therapeutic development. Sequence-based deep learning models for predicting protein-ligand binding residues have gained attention due to their scalability and ability to operate without relying on structural information. However, most existing methods primarily focus on protein sequence information without considering ligand information, even though binding residues are inherently defined through interactions with specific ligands. To address this, we propose a ligand-aware sequence-based binding residue prediction model that explicitly incorporates both residue-level information from protein sequences and ligand information. The proposed model achieved significant improvements in the prediction of ligand-binding residues, outperforming both existing sequence-based and structure-based baselines. Furthermore, pockets defined by the ligand-binding residues predicted by our model led to a stronger and more stable binding affinity compared to existing tools. These results demonstrate that our model shows significant potential for applications in virtual screening and drug discovery. Our source code is publicly available at https://github.com/GoldRiver0/LiBRe.