Implementation of a new detector tool of drug drug interactions



drug-drug interactions, litterature based approaches, prediction, dynamic filter predictor


Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients more likely with elder people , with serious consequences. so it is urgent to use computer methods to solve the problem. Thereare traditional approaches to identify known drug interactions linked to tools including the prediction unknown drug interactions. In this paper, we will present and define a new implementation of an application initially with an excel projection this application is made with the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the general information , then briefly describe methods , and summarize with the application demo of some important disease for elder patient . Finally, we discuss the importance of the dynamic filter in the Healthcare sector . This paper aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to more develop this application

Author Biographies

Marouane Bnitir, I.M. Sechenov First Moscow State Medical University

Graduate student, public health department.

Konstantin Aleksandrovich Koshechkin, I.M. Sechenov First Moscow State Medical

PhD, Associate Professor in the Department of the information and Internet Technologies of the Institute of Digital medicine


Maher, Robert L, et al. “Clinical Consequences of Polypharmacy in Elderly.” Expert Opinion on Drug Safety, vol. 13, no. 1, 2013, pp. 57–65.,

“Types and Phases of Clinical Trials: What Are Clinical Trial Phases?” American Cancer Society,

Dechanont, Supinya, et al. “Hospital Admissions/Visits Associated with Drug-Drug Interactions: A Systematic Review and Meta-Analysis.” Pharmacoepidemiology and Drug Safety, vol. 23, no. 5, 2014, pp. 489–497.,

Feng, Yue-Hua, and Shao-Wu Zhang. “Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.” Molecules, vol. 27, no. 9, 2022, p. 3004.,

Baxter, Karen, and Jennifer M Sharp. “Adverse Drug Interactions.” Adverse Drug Reaction Bulletin, &NA; no. 248, 2008, pp. 951–954.,

Lapham, Robert. “Adverse Drug Reactions (Adrs).” Drug Calculations for Nurses, 2021, pp. 233–240.,

Zhou, Quan, et al. “Pharmacokinetic Drug Interaction Profile of Omeprazole with Adverse Consequences and Clinical Risk Management.” Therapeutics and Clinical Risk Management, 2013, p. 259.,

Yue-Hua, Feng, et al. “DPDDI: A Deep Predictor for Drug-Drug Interactions.” 2020,

Lee, Chun Yen, and Yi-Ping Phoebe Chen. “Prediction of Drug Adverse Events Using Deep Learning in Pharmaceutical Discovery.” Briefings in Bioinformatics, vol. 22, no. 2, 2020, pp. 1884–1901.,

Lebedev, Georgy, et al. “Technology of Supporting Medical Decision-Making Using Evidence-Based Medicine and Artificial Intelligence.” Procedia Computer Science, vol. 176, 2020, pp. 1703–1712.,

He, Bing, et al. “Combination Therapeutics in Complex Diseases.” Journal of Cellular and Molecular Medicine, vol. 20, no. 12, 2016, pp. 2231–2240.,

Vilar, Santiago, et al. “Detection of Drug–Drug Interactions through Data Mining Studies Using Clinical Sources, Scientific Literature and Social Media.” Briefings in Bioinformatics, vol. 19, no. 5, 2017, pp. 863–877.,

Rohani, Narjes, and Changiz Eslahchi. “Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity.” Scientific Reports, vol. 9, no. 1, 2019,

The National Council on Aging,

Han, Ke, et al. “A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning.” Frontiers in Pharmacology, vol. 12, 2022,

Lo, H. Z., & Nazeri, Z. (2013). Mining adverse drug reactions from electronic health records. 2013 IEEE 13th International Conference on Data Mining Workshops.

Stauth, T. (2022, March 3). Ebook: The little book of big changes in AI-powered drug discovery. DrugBank Blog. Retrieved May 19, 2022, from

Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2017 Nov 8. doi: 10.1093/nar/gkx1037