Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery
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arXiv
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| Format: | Preprint |
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2025
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| author | Lan, Jing Ding, Hexiao Chen, Hongzhao Jiang, Yufeng Ng, Nga-Chun Cheng, Gerald W. Y. Li, Zongxi Cai, Jing Lin, Liang-ting Yoo, Jung Sun |
| author_facet | Lan, Jing Ding, Hexiao Chen, Hongzhao Jiang, Yufeng Ng, Nga-Chun Cheng, Gerald W. Y. Li, Zongxi Cai, Jing Lin, Liang-ting Yoo, Jung Sun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery Lan, Jing Ding, Hexiao Chen, Hongzhao Jiang, Yufeng Ng, Nga-Chun Cheng, Gerald W. Y. Li, Zongxi Cai, Jing Lin, Liang-ting Yoo, Jung Sun Biomolecules Artificial Intelligence Machine Learning 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. |
| title | Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.01799 |