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

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.

SUBMITTER: Yun GH 

PROVIDER: S-EPMC12874921 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature