🤖 AI Summary
This work addresses the limited expressiveness and generalizability of existing drug–target interaction (DTI) prediction models that rely on single-modality representations. The authors propose Tensor-DTI, a novel framework that, for the first time, integrates multimodal embeddings—comprising molecular graphs, protein language models, and binding site predictions—through contrastive learning. A siamese dual-encoder architecture is employed to model chemico-structural interaction features. Tensor-DTI significantly improves performance in distinguishing interacting from non-interacting pairs, outperforming state-of-the-art methods across multiple DTI benchmarks. It yields plausible hit distributions in billion-scale virtual screening against CDK2 and substantially reduces the cost of discovering high-affinity ligands for out-of-family targets. Moreover, the model demonstrates strong interpretability and cross-task extensibility, as evidenced by its applicability to protein–RNA and peptide–protein interactions.
📝 Abstract
Accurate drug-target interaction (DTI) prediction is essential for computational drug discovery, yet existing models often rely on single-modality predefined molecular descriptors or sequence-based embeddings with limited representativeness. We propose Tensor-DTI, a contrastive learning framework that integrates multimodal embeddings from molecular graphs, protein language models, and binding-site predictions to improve interaction modeling. Tensor-DTI employs a siamese dual-encoder architecture, enabling it to capture both chemical and structural interaction features while distinguishing interacting from non-interacting pairs. Evaluations on multiple DTI benchmarks demonstrate that Tensor-DTI outperforms existing sequence-based and graph-based models. We also conduct large-scale inference experiments on CDK2 across billion-scale chemical libraries, where Tensor-DTI produces chemically plausible hit distributions even when CDK2 is withheld from training. In enrichment studies against Glide docking and Boltz-2 co-folder, Tensor-DTI remains competitive on CDK2 and improves the screening budget required to recover moderate fractions of high-affinity ligands on out-of-family targets under strict family-holdout splits. Additionally, we explore its applicability to protein-RNA and peptide-protein interactions. Our findings highlight the benefits of integrating multimodal information with contrastive objectives to enhance interaction-prediction accuracy and to provide more interpretable and reliability-aware models for virtual screening.