Drug discovery remains time-consuming, labor-intensive, and expensive, often requiring years and substantial investment per drug candidate. Predicting compound-protein interactions (CPIs) is a critical component in this process, enabling the identification of molecular interactions between drug candidates and target proteins. Recent deep learning methods have successfully modeled CPIs at the atomic level, achieving improved efficiency and accuracy over traditional energy-based approaches. However, these models do not always align with chemical realities, as molecular fragments (motifs or functional groups) typically serve as the primary units of biological recognition and binding. In this paper, we propose Phi-former, a pairwise hierarchical interaction representation learning method that addresses this gap by incorporating the biological role of motifs in CPIs. Phi-former represents compounds and proteins hierarchically and employs a pairwise pre-training framework to model interactions systematically across atom-atom, motif-motif, and atom-motif levels, reflecting how biological systems recognize molecular partners. We design intra-level and inter-level learning pipelines that make different interaction levels mutually beneficial. Experimental results demonstrate that Phi-former achieves superior performance on CPI-related tasks. A case study shows that our method accurately identifies specific atoms or motifs activated in CPIs, providing interpretable model explanations. These insights may guide rational drug design and support precision medicine applications.

翻译：药物发现过程依然耗时、费力且昂贵，通常每个候选药物需要数年时间和大量投入。预测化合物-蛋白质相互作用（CPIs）是这一过程中的关键环节，能够识别候选药物与靶蛋白之间的分子相互作用。近期的深度学习方法已成功在原子层面建模CPIs，相比传统的基于能量的方法实现了更高的效率和准确性。然而，这些模型并不总是符合化学现实，因为分子片段（基序或官能团）通常是生物识别与结合的主要单元。本文提出Phi-Former，一种成对层次化相互作用表示学习方法，通过纳入基序在CPIs中的生物学作用来弥补这一差距。Phi-Former对化合物和蛋白质进行层次化表示，并采用成对预训练框架，系统地在原子-原子、基序-基序及原子-基序层面建模相互作用，从而反映生物系统识别分子伴侣的方式。我们设计了层内与层间学习流程，使不同相互作用层面能够相互促进。实验结果表明，Phi-Former在CPI相关任务上取得了优越性能。案例研究表明，我们的方法能准确识别在CPIs中被激活的特定原子或基序，提供可解释的模型说明。这些见解有望指导合理药物设计并支持精准医学应用。