Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this work, we introduce Contrastive Geometric Learning for Unified Computational Drug Design (ConGLUDe), a single contrastive geometric model that unifies structure- and ligand-based training. ConGLUDe couples a geometric protein encoder that produces whole-protein representations and implicit embeddings of predicted binding sites with a fast ligand encoder, removing the need for pre-defined pockets. By aligning ligands with both global protein representations and multiple candidate binding sites through contrastive learning, ConGLUDe supports ligand-conditioned pocket prediction in addition to virtual screening and target fishing, while being trained jointly on protein-ligand complexes and large-scale bioactivity data. Across diverse benchmarks, ConGLUDe achieves competitive zero-shot virtual screening performance, substantially outperforms existing methods on a challenging target fishing task, and demonstrates state-of-the-art ligand-conditioned pocket selection. These results highlight the advantages of unified structure-ligand training and position ConGLUDe as a step toward general-purpose foundation models for drug discovery.

翻译：基于结构的计算药物设计与基于配体的计算药物设计传统上依赖于互不相交的数据源和建模假设，限制了它们的大规模联合使用。本研究提出了用于统一计算药物设计的对比几何学习模型（ConGLUDe），这是一个单一的对比几何模型，统一了基于结构与基于配体的训练。ConGLUDe将生成全蛋白表征及预测结合位点隐式嵌入的几何蛋白编码器与快速配体编码器相耦合，消除了对预定义结合口袋的需求。通过对比学习将配体与全局蛋白表征及多个候选结合位点对齐，ConGLUDe除了支持虚拟筛选和靶标垂钓外，还能进行配体条件化口袋预测，同时可在蛋白-配体复合物和大规模生物活性数据上进行联合训练。在多样化的基准测试中，ConGLUDe实现了具有竞争力的零样本虚拟筛选性能，在具有挑战性的靶标垂钓任务上显著优于现有方法，并展示了最先进的配体条件化口袋选择能力。这些结果凸显了统一结构-配体训练的优势，并将ConGLUDe定位为迈向通用药物发现基础模型的重要一步。