A novel deep sequential learning architecture for drug drug interaction prediction using DDINet
Abstract Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This article proposes a novel deep sequential learning architecture called DDINet to predict and classify DDIs between pairs of drugs based on di...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93952-z |
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| author | Anindya Halder Biswanath Saha Moumita Roy Sukanta Majumder |
| author_facet | Anindya Halder Biswanath Saha Moumita Roy Sukanta Majumder |
| author_sort | Anindya Halder |
| collection | DOAJ |
| description | Abstract Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This article proposes a novel deep sequential learning architecture called DDINet to predict and classify DDIs between pairs of drugs based on different mechanisms viz., Excretion, Absorption, Metabolism, and Excretion rate (higher serum level) etc. Chemical features such as Hall Smart, Amino Acid count and Carbon types are extracted from each drug (pairs) to apply as an input to the proposed model. Proposed DDINet incorporates attention mechanism and deep sequential learning architectures, such as Long Short-Term Memory and gated recurrent unit. It utilizes the Rcpi toolkit to extract biochemical features of drugs from their chemical composition in Simplified Molecular-Input Line-Entry System format. Experiments are conducted on publicly available DDI datasets from DrugBank and Kaggle. The model’s efficacy in predicting and classifying DDIs is evaluated using various performance measures. The experimental results show that DDINet outperformed eight counterpart techniques achieving $$95.42\%$$ overall accuracy which is also statistically confirmed by Confidence Interval tests and paired t-tests. This architecture may act as an effective computational technique for drug drug interaction with respect to mechanism which may act as a complementary tool to reduce costly wet lab experiments for DDI prediction and classification. |
| format | Article |
| id | doaj-art-e27e153701694b2f9922cbe2741edff1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e27e153701694b2f9922cbe2741edff12025-08-20T02:52:16ZengNature PortfolioScientific Reports2045-23222025-03-0115111510.1038/s41598-025-93952-zA novel deep sequential learning architecture for drug drug interaction prediction using DDINetAnindya Halder0Biswanath Saha1Moumita Roy2Sukanta Majumder3Department of Computer Application, School of Technology, North-Eastern Hill University, Tura CampusDepartment of Computer Application, School of Technology, North-Eastern Hill University, Tura CampusDepartment of Computer Science and Engineering, University of KalyaniDepartment of Computer Science and Engineering, University of KalyaniAbstract Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This article proposes a novel deep sequential learning architecture called DDINet to predict and classify DDIs between pairs of drugs based on different mechanisms viz., Excretion, Absorption, Metabolism, and Excretion rate (higher serum level) etc. Chemical features such as Hall Smart, Amino Acid count and Carbon types are extracted from each drug (pairs) to apply as an input to the proposed model. Proposed DDINet incorporates attention mechanism and deep sequential learning architectures, such as Long Short-Term Memory and gated recurrent unit. It utilizes the Rcpi toolkit to extract biochemical features of drugs from their chemical composition in Simplified Molecular-Input Line-Entry System format. Experiments are conducted on publicly available DDI datasets from DrugBank and Kaggle. The model’s efficacy in predicting and classifying DDIs is evaluated using various performance measures. The experimental results show that DDINet outperformed eight counterpart techniques achieving $$95.42\%$$ overall accuracy which is also statistically confirmed by Confidence Interval tests and paired t-tests. This architecture may act as an effective computational technique for drug drug interaction with respect to mechanism which may act as a complementary tool to reduce costly wet lab experiments for DDI prediction and classification.https://doi.org/10.1038/s41598-025-93952-zDrug drug interactionDeep learningAttention mechanismRecurrent neural networkGated recurrent unit |
| spellingShingle | Anindya Halder Biswanath Saha Moumita Roy Sukanta Majumder A novel deep sequential learning architecture for drug drug interaction prediction using DDINet Scientific Reports Drug drug interaction Deep learning Attention mechanism Recurrent neural network Gated recurrent unit |
| title | A novel deep sequential learning architecture for drug drug interaction prediction using DDINet |
| title_full | A novel deep sequential learning architecture for drug drug interaction prediction using DDINet |
| title_fullStr | A novel deep sequential learning architecture for drug drug interaction prediction using DDINet |
| title_full_unstemmed | A novel deep sequential learning architecture for drug drug interaction prediction using DDINet |
| title_short | A novel deep sequential learning architecture for drug drug interaction prediction using DDINet |
| title_sort | novel deep sequential learning architecture for drug drug interaction prediction using ddinet |
| topic | Drug drug interaction Deep learning Attention mechanism Recurrent neural network Gated recurrent unit |
| url | https://doi.org/10.1038/s41598-025-93952-z |
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