Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning

• URL: http://arxiv.org/abs/2601.05792v1
• Date: Fri, 09 Jan 2026 13:39:49 GMT
• Title: Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
• Authors: Manel Gil-Sorribes, Júlia Vilalta-Mor, Isaac Filella-Mercè, Robert Soliva, Álvaro Ciudad, Víctor Guallar, Alexis Molina,
• Abstract summary: We propose a contrastive learning framework that integrates multimodal embeddings from molecular graphs, protein language models, and binding-site predictions to improve interaction modeling.<n>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 splits.
• Score: 0.015229507502478598
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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