Kumar Shukla P, Kumar Shukla P, Sharma P, Rawat P, Samar J, Moriwal R, Kaur M. Efficient prediction of drug-drug interaction using deep learning models.
IET Syst Biol 2020;
14:211-216. [PMID:
32737279 PMCID:
PMC8687321 DOI:
10.1049/iet-syb.2019.0116]
[Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 04/09/2020] [Accepted: 04/22/2020] [Indexed: 12/23/2022] Open
Abstract
A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.
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