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. 2025 Jul 20;26(1):185.
doi: 10.1186/s12859-025-06141-0.

DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit

Kavipriya Gananathan  1 , D Manjula  2 , Vijayan Sugumaran  3   4
Affiliations

DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit

Kavipriya Gananathan et al. BMC Bioinformatics. .

Abstract

Background: Identification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) techniques become essential. The prediction of DTI is a challenging process due to the absence of known drug-target relationship and no experimentally verified negative samples. The datasets with limited or unbalanced data, do not perform well. The models that use heterogeneous networks, non-linear fusion techniques, and heuristic similarity selection may need a lot of computational power and experience to implement and fine-tune. The latest developments in machine learning (ML) and deep learning (DL) models can be employed for effective DTI prediction process.

Results: To that end, this study develops a novel DTI Prediction model, namely, DTIP-WINDGRU Drug-Target Interaction Prediction with Wind-Enhanced GRU. The major aim is to determine the DTIs in both labelled and unlabelled samples accurately compared to traditional wet lab experiments. To accomplish this, the proposed DTIP-WINDGRU model primarily performs pre-processing and class labelling. In addition, drug-to-drug (D-D) and target-to-target (T-T) interactions are employed to initialize the weights of the GRU model and are employed for the, DTI prediction process. Finally, the Wind Driven Optimization (WDO) algorithm is utilized to optimally choose the hyperparameters involved in the GRU model.

Conclusions: For ensuring the effectual prediction results of the DTIP-WINDGRU model, a widespread experimentation process was carried out using four datasets. This comprehensive comparative study highlighted the better performance of the DTIP-WINDGRU model over existing techniques.

Keywords: Deep learning; Drug-target interaction; Gated recurrent unit; Prediction model; Wind driven optimization.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Process Model for the DTIP-WINDGRU Approach
Fig. 2
Fig. 2
Framework of GRU
Fig. 3
Fig. 3
Labeled Data Analysis of DTIP-WINDGRU model. a Enzyme dataset, b G Protein-Coupled Receptors dataset, c ION Channel dataset, d Nuclear Receptor dataset, showing labeled and unlabeled instances at various top k% values
Fig. 4
Fig. 4
AUC and AUPR analysis of DTIP-WINDGRU model on various CV_SEEDs values. a Enzyme dataset, b G Protein-Coupled Receptors dataset, c ION Channel dataset, d Nuclear Receptor dataset, displaying AUC and AUPR performance across five cross-validation sets
Fig. 5
Fig. 5
AUPR analysis of DTIP-WINDGRU model on Enzyme and GPCR datasets
Fig. 6
Fig. 6
AUPR analysis of DTIP-WINDGRU model on ION channel and nuclear receptor datasets
Fig. 7
Fig. 7
Ablation results of AUPR analysis with and without WDO in all four datasets
Fig. 8
Fig. 8
Ablation results of AUC analysis with and without WDO in all four datasets
See this image and copyright information in PMC

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