TextDTI: A Multimodal Context Representation Learning Framework for Drug-Target Interaction Prediction.
TL;DR
This paper proposes TextDTI, a multimodal framework that simultaneously exploits sequential and structural representations and enhances feature alignment through adversarial learning and contrastive loss, resulting in robust and high-performance DTI prediction.
Abstract
Predicting Drug-Target Interactions (DTIs) is a crucial task in drug discovery. Recent advances in deep learning, particularly the application of Large Language Models (LLMs), have shown promise in encoding sequential information from SMILES strings and protein sequences. However, integrating these diverse modalities remains a challenge. In this paper, we propose TextDTI, a multimodal framework that simultaneously exploits sequential and structural representations. First, TextDTI utilizes Pretrained Language Models (PLMs) to generate functional description texts for proteins represented by their amino acid sequences, which includes biological functions, molecular mechanisms, and pathway involvement. Next, the generated texts and the SMILES sequences of drugs are encoded into corresponding feature representations by other separate LLMs. Third, drug and target characteristics are fused through convolutional and graph-based modules. Finally, unidentified drug-target interactions are classified using a multilayer perceptron neural network. We further enhance feature alignment through adversarial learning and contrastive loss, resulting in robust and high-performance DTI prediction. Experiments conducted on multiple data sets in both single-domain and cross-domain settings demonstrate that our model outperforms other baseline methods. The source code and data sets are available at https://github.com/xiaoyiliu-usc/TextDTI.