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Contextual Representation Learning With ResNet Refinement and Deep Classification for Enhanced Drug Target Interaction Prediction

Essmily Simon Sanjay S. Bankapur
Jul 2026 · Chemical Biology and Drug Design · Vol 108 · 0 citations · 39 references
Medicine

TL;DR

A novel deep learning framework is proposed that leverages pre‐trained BERT‐based language models to extract contextual embeddings from protein and drug sequences and refined using a proposed dedicated ResNet‐based subnetwork to preserve intrinsic biochemical characteristics.

Abstract

Accurate prediction of drug target interactions (DTIs) plays a pivotal role in drug discovery and repositioning. However, it remains challenging due to the structural complexity of proteins and small‐molecule compounds, along with the limited generalisation capability of existing computational approaches. Drug target interaction prediction is important in computer‐aided drug design and drug repurposing, especially for complex diseases where multiple targets are involved. This study aims to develop a robust deep learning framework that enhances DTI prediction accuracy by effectively capturing contextual and biochemical features from both protein and drug representations. Four standard DTI datasets and a combined drug repurposing dataset are used to learn interaction patterns across multiple targets. We propose a novel deep learning framework that leverages pre‐trained BERT‐based language models to extract contextual embeddings from protein and drug sequences. These modality‐specific representations are refined using a proposed dedicated ResNet‐based subnetwork to preserve intrinsic biochemical characteristics. The refined embeddings are subsequently integrated and passed through a proposed deep feedforward neural network for final DTI prediction. The proposed model was evaluated on four benchmark datasets, namely DrugBank, Caenorhabditis elegans, BindingDB and GPCR. Experimental results demonstrate consistent performance improvements over baseline methods, including an F1‐score gain of ~6.6% on the GPCR dataset and a 3.7% increase in classification accuracy on BindingDB. Stable F1‐score improvements were also observed on DrugBank (0.4%) and Caenorhabditis elegans (0.8%). Statistical validation using paired ω‐tests at a 5% significance level confirms the improvements are significant. Evaluation on an independent drug repurposing dataset achieved a 6.7% performance gain over existing approaches. The results demonstrate that the proposed framework effectively captures contextual and structural information, leading to improved prediction accuracy and generalisation, highlighting its robustness and practical applicability for drug discovery and drug repurposing.

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