# TriDTI: tri-modal representation learning with cross-modal alignment for drug–target interaction prediction

## TL;DR

TriDTI is a new AI framework that improves drug-target interaction prediction by combining three types of data for drugs and proteins.

## Contribution

TriDTI introduces a novel tri-modal representation learning framework with cross-modal alignment for drug–target interaction prediction.

## Key findings

- TriDTI outperforms existing state-of-the-art methods on three benchmark datasets.
- The framework shows robust generalization in cold-start scenarios involving novel drugs, targets, and bindings.

## Abstract

The rapid advancement of artificial intelligence has positioned drug–target interaction (DTI) prediction as a promising approach in drug screening and drug discovery. Recent research has attempted to use pharmacological multimodal information to increase prediction accuracy. However, existing approaches are limited in fully utilizing more than three modalities, primarily due to information loss during the modality integration process. To overcome this challenge, we propose TriDTI, a novel framework that incorporates three modalities for both drugs and proteins. Specifically, TriDTI integrates structural, sequential, and relational modalities from both entities. To mitigate information loss during integration, we employ projection and cross-modal contrastive learning for modality alignment. Furthermore, we design a fusion strategy that combines soft attention and cross-attention to effectively integrate multimodal representations. Extensive experiments on three benchmark datasets demonstrate that TriDTI consistently achieves superior performance to existing state-of-the-art approaches in DTI prediction. Moreover, TriDTI exhibits a robust generalization ability across three challenging cold-start scenarios, effectively predicting interactions involving novel drugs, targets, and bindings. These results highlight the potential of TriDTI as a robust and practical framework for facilitating drug discovery. The source codes and datasets are publicly accessible at https://github.com/knhc1234/TriDTI.