S$^2$Drug：在虚拟筛选中桥接蛋白质序列与三维结构的对比表示学习
S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening
AI总结：
S$^2$Drug提出两阶段对比学习框架，融合蛋白质序列与三维结构信息，并通过结合位点预测辅助任务提升虚拟筛选性能。
摘要：
虚拟筛选（VS）是药物发现中的一项关键任务，专注于识别与特定蛋白质口袋结合的小分子配体。现有的深度学习方法，从早期的回归模型到近期的对比学习方法，主要依赖结构数据，却忽视了蛋白质序列；而蛋白质序列更易获取，并能增强泛化能力。然而，直接整合蛋白质序列面临挑战，因为大规模蛋白质-配体数据集中存在冗余和噪声。为了解决这些局限性，我们提出S$^2$Drug，一个两阶段框架，在蛋白质-配体对比表示学习中显式地结合蛋白质序列信息和三维结构上下文。在第一阶段，我们使用基于ESM2的主干网络在ChemBL上进行蛋白质序列预训练，并结合定制化的数据采样策略，以在蛋白质侧和配体侧同时减少冗余和噪声。在第二阶段，我们在PDBBind上进行微调，通过残基级门控模块融合序列和结构信息，同时引入辅助的结合位点预测任务。该辅助任务引导模型在蛋白质序列中准确定位结合残基，并捕捉其三维空间排布，从而优化蛋白质-配体匹配。在多个基准测试上，S$^2$Drug持续提升虚拟筛选性能，并在结合位点预测上取得优异结果，证明了在对比学习中桥接序列与结构的价值。
英文摘要：
Virtual screening (VS) is an essential task in drug discovery, focusing on the identification of small-molecule ligands that bind to specific protein pockets. Existing deep learning methods, from early regression models to recent contrastive learning approaches, primarily rely on structural data while overlooking protein sequences, which are more accessible and can enhance generalizability. However, directly integrating protein sequences poses challenges due to the redundancy and noise in large-scale protein-ligand datasets. To address these limitations, we propose \textbf{S$^2$Drug}, a two-stage framework that explicitly incorporates protein \textbf{S}equence information and 3D \textbf{S}tructure context in protein-ligand contrastive representation learning. In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone, combined with a tailored data sampling strategy to reduce redundancy and noise on both protein and ligand sides. In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module, while introducing an auxiliary binding site prediction task. This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement, thereby refining protein-ligand matching. Across multiple benchmarks, S$^2$Drug consistently improves virtual screening performance and achieves strong results on binding site prediction, demonstrating the value of bridging sequence and structure in contrastive learning.
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