Zero-Shot Learning with Subsequence Reordering Pretraining for Compound-Protein Interaction

TL;DR

This paper introduces a novel subsequence reordering pretraining method for protein representations to improve zero-shot compound-protein interaction prediction, especially in data-scarce scenarios, enhancing drug discovery processes.

Contribution

It proposes a new pretraining approach that explicitly models subsequence dependencies and uses length-variable augmentation to boost zero-shot CPI prediction performance.

Findings

Improves baseline CPI prediction accuracy in zero-shot scenarios.

Outperforms existing pretraining models in data-limited conditions.

Demonstrates effectiveness on real-world drug development tasks.

Abstract

Given the vastness of chemical space and the ongoing emergence of previously uncharacterized proteins, zero-shot compound-protein interaction (CPI) prediction better reflects the practical challenges and requirements of real-world drug development. Although existing methods perform adequately during certain CPI tasks, they still face the following challenges: (1) Representation learning from local or complete protein sequences often overlooks the complex interdependencies between subsequences, which are essential for predicting spatial structures and binding properties. (2) Dependence on large-scale or scarce multimodal protein datasets demands significant training data and computational resources, limiting scalability and efficiency. To address these challenges, we propose a novel approach that pretrains protein representations for CPI prediction tasks using subsequence reordering, explicitly capturing the dependencies between protein subsequences. Furthermore, we apply length-variable protein augmentation to ensure excellent pretraining performance on small training datasets. To evaluate the model's effectiveness and zero-shot learning ability, we combine it with various baseline methods. The results demonstrate that our approach can improve the baseline model's performance on the CPI task, especially in the challenging zero-shot scenario. Compared to existing pre-training models, our model demonstrates superior performance, particularly in data-scarce scenarios where training samples are limited. Our implementation is available at https://github.com/Hoch-Zhang/PSRP-CPI.