{"ID":2887361,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01799","arxiv_id":"2508.01799","title":"Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery","abstract":"Accurate prediction of protein-ligand interactions is essential for computer-aided drug discovery. However, existing methods often fail to capture solvent-dependent conformational changes and lack the ability to jointly learn multiple related tasks. To address these limitations, we introduce a pre-training method that incorporates ligand conformational ensembles generated under diverse solvent conditions as augmented input. This design enables the model to learn both structural flexibility and environmental context in a unified manner. The training process integrates molecular reconstruction to capture local geometry, interatomic distance prediction to model spatial relationships, and contrastive learning to build solvent-invariant molecular representations. Together, these components lead to significant improvements, including a 3.7% gain in binding affinity prediction, an 82% success rate on the PoseBusters Astex docking benchmarks, and an area under the curve of 97.1% in virtual screening. The framework supports solvent-aware, multi-task modeling and produces consistent results across benchmarks. A case study further demonstrates sub-angstrom docking accuracy with a root-mean-square deviation of 0.157 angstroms, offering atomic-level insight into binding mechanisms and advancing structure-based drug design.","short_abstract":"Accurate prediction of protein-ligand interactions is essential for computer-aided drug discovery. However, existing methods often fail to capture solvent-dependent conformational changes and lack the ability to jointly learn multiple related tasks. To address these limitations, we introduce a pre-training method that...","url_abs":"https://arxiv.org/abs/2508.01799","url_pdf":"https://arxiv.org/pdf/2508.01799v2","authors":"[\"Jing Lan\",\"Hexiao Ding\",\"Hongzhao Chen\",\"Yufeng Jiang\",\"Nga-Chun Ng\",\"Gerald W. Y. Cheng\",\"Zongxi Li\",\"Jing Cai\",\"Liang-ting Lin\",\"Jung Sun Yoo\"]","published":"2025-08-03T15:25:42Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}