Compound-Protein Interaction (CPI) prediction aims to predict the pattern and
strength of compound-protein interactions for rational drug discovery. Existing
deep learning-based methods utilize only the single modality of protein
sequences or structures and lack the co-modeling of the joint distribution of
the two modalities, which may lead to significant performance drops in complex
real-world scenarios due to various factors, e.g., modality missing and domain
shifting. More importantly, these methods only model protein sequences and
structures at a single fixed scale, neglecting more fine-grained multi-scale
information, such as those embedded in key protein fragments. In this paper, we
propose a novel multi-scale Protein Sequence-structure Contrasting framework
for CPI prediction (PSC-CPI), which captures the dependencies between protein
sequences and structures through both intra-modality and cross-modality
contrasting. We further apply length-variable protein augmentation to allow
contrasting to be performed at different scales, from the amino acid level to
the sequence level. Finally, in order to more fairly evaluate the model
generalizability, we split the test data into four settings based on whether
compounds and proteins have been observed during the training stage. Extensive
experiments have shown that PSC-CPI generalizes well in all four settings,
particularly in the more challenging ``Unseen-Both" setting, where neither
compounds nor proteins have been observed during training. Furthermore, even
when encountering a situation of modality missing, i.e., inference with only
single-modality protein data, PSC-CPI still exhibits comparable or even better
performance than previous approaches.

提出了一种新颖的多尺度蛋白质序列 - 结构对比 (CPI) 预测框架 (PSC-CPI)，通过在蛋白质序列和结构之间进行内部模态和交叉模态对比，从多个尺度捕捉了蛋白质序列和结构之间的依赖关系，并且具有良好的模型普适性。