通过溶剂感知增强实现多任务学习，提升药物发现中蛋白-配体相互作用预测精度
Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery
- 利用不同溶剂条件生成的配体构象集合作为增强输入，融合多任务训练
- 在结合亲和力预测上提升3.7%，虚拟筛选AUC达97.1%
- 适合结构基药物设计、分子对接及需要考虑环境影响的研究者
准确预测蛋白-配体相互作用对计算机辅助药物发现至关重要。然而，现有方法常无法捕捉溶剂依赖的构象变化，且缺乏联合学习多个相关任务的能力。为此，我们提出一种预训练方法，将不同溶剂条件下生成的配体构象集合作为增强输入，使模型统一学习结构灵活性与环境上下文。训练过程整合分子重建以捕捉局部几何结构、原子间距离预测以建模空间关系，以及对比学习以构建溶剂不变的分子表示。这些组件协同作用带来显著提升：结合亲和力预测性能提高3.7%，在PoseBusters Astex对接基准上取得82%的成功率，虚拟筛选的曲线下面积（AUC）达到97.1%。该框架支持溶剂感知的多任务建模，在多个基准测试中表现一致。案例研究进一步展示亚埃级对接精度，均方根偏差为0.157埃，为结合机制提供原子级洞察，推动基于结构的药物设计发展。
原文摘要 · Abstract (English)
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.
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