AMFPMC -- An improved method of detecting multiple types of drug-drug interactions using only known drug-drug interactions

• URL: http://arxiv.org/abs/2302.03355v1
• Date: Tue, 7 Feb 2023 09:57:54 GMT
• Title: AMFPMC -- An improved method of detecting multiple types of drug-drug interactions using only known drug-drug interactions
• Authors: Bar Vered and Guy Shtar and Lior Rokach and Bracha Shapira
• Abstract summary: Adverse drug interactions are largely preventable causes of medical accidents. The detection of drug interactions in a lab, prior to a drug's use in medical practice, is essential. Machine learning techniques can provide an efficient and accurate means of predicting possible drug-drug interactions.
• Score: 18.027128141189355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adverse drug interactions are largely preventable causes of medical accidents, which frequently result in physician and emergency room encounters. The detection of drug interactions in a lab, prior to a drug's use in medical practice, is essential, however it is costly and time-consuming. Machine learning techniques can provide an efficient and accurate means of predicting possible drug-drug interactions and combat the growing problem of adverse drug interactions. Most existing models for predicting interactions rely on the chemical properties of drugs. While such models can be accurate, the required properties are not always available.

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