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。

PSC-CPI: 高效且可泛化的多尺度蛋白质序列 - 结构对比用于化合物 - 蛋白相互作用预测

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for  Efficient and Generalizable Compound-Protein Interaction Prediction

Motivation: Drug discovery demands rapid quantification of compound-protein
interaction (CPI). However, there is a lack of methods that can predict
compound-protein affinity from sequences alone with high applicability,
accuracy, and interpretability.
Results: We present a seamless integration of domain knowledges and
learning-based approaches. Under novel representations of
structurally-annotated protein sequences, a semi-supervised deep learning model
that unifies recurrent and convolutional neural networks has been proposed to
exploit both unlabeled and labeled data, for jointly encoding molecular
representations and predicting affinities. Our representations and models
outperform conventional options in achieving relative error in IC$_{50}$ within
5-fold for test cases and 20-fold for protein classes not included for
training. Performances for new protein classes with few labeled data are
further improved by transfer learning. Furthermore, separate and joint
attention mechanisms are developed and embedded to our model to add to its
interpretability, as illustrated in case studies for predicting and explaining
selective drug-target interactions. Lastly, alternative representations using
protein sequences or compound graphs and a unified RNN/GCNN-CNN model using
graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.
Availability: Data and source codes are available at
this https URL
Supplementary Information: Supplementary data are available at
this http URL

利用领域知识和深度学习方法，提出了一种半监督深度学习模型，旨在从蛋白质序列中预测化合物与蛋白质相互作用并对其进行解释，表现优于传统方法，并采用转移学习进一步提高了新型蛋白质的预测效果以及对筛选药物的解释性能。