A Machine Learning-Based Method for Predicting unknown Pharmacointeractions
Jayshree Ghorpade Aher1, Shreyans Magdum2, Nandini Sonkusakle3, Parul Jaiswal4, Raj Shah5
1Jayshree Ghorpade Aher, Assistant Professor, Department Computer Engineering, MIT WPU University, Pune (Maharashtra), India.
2Shreyans Magdum, Department Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
3Parul Jaiswal, Department Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
4Nandini Sonkusakle, Department Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
5Raj Shah, Department Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
Manuscript received on 08 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 31 December 2019 | PP: 662-665 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11071292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1107.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A lot of research has been done on the efficacy of machine learning algorithms in predicting the pharmacological interference between two drugs. Ordinarily, this interference depends on many factors such as the taxonomical, chemical, pharmacological or genomic similarities between the two drugs. Nevertheless, a lot of adverse events (AEs) are reported every year, due to the simultaneous consumption of two or more drugs. Much research has been conducted on the accuracy of the interference prediction based on these factors, each differing in the algorithms and factors used. In this publication, we propose a machine learning-based approach to predict undiscovered drug-drug interactions based on a few of the impacting factors, for better results and thus, help minimize the potential harm that can be caused to society.
Keywords: Drug-drug Interactions, Pharmacointeraction, Machine Learning, DDI.
Scope of the Article: Machine Learning