Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
Track: Machine learning: computational method and/or computational results
Nature Biotechnology: Yes
Keywords: drug-target interaction, contrastive learning, embedding, affinity prediction, pocket specificity
TL;DR: We present a contrastive learning framework to perform DTI predictions with pocket specificity and enhanced efficiency.
Abstract: Accurate drug-target interaction (DTI) prediction is essential for computational drug discovery, yet existing models often rely on pre-defined molecular descriptors or sequence-based embeddings with limited generalizability. 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, including BIOSNAP, BindingDB, DAVIS, and PLINDER, demonstrate that Tensor-DTI outperforms existing sequence-based and graph-based models. Additionally, we assess its generalization to unseen drugs and proteins and explore its applicability to protein-RNA and peptide-protein interactions. Our findings highlight the benefits of integrating structural information with contrastive objectives to enhance interaction prediction accuracy and model interpretability.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Presenter: ~Manel_Gil-Sorribes1
Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.
Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.
Submission Number: 78
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