MID: Medicines Information Dataset
Description
Numerous studies on medicines are conducted day by day. To address shortcomings of medicines information generation, prediction, and classification models, the authors introduce a large medicines information dataset of textual data. For this motivation, the authors named our dataset ‘MID’. • Value of the data - MID is the largest, to our knowledge, available and representative Medicines Information Dataset (MID) for a wide variety of drugs. It includes the names of over 192k medicines, making it a comprehensive collection of pharmaceutical products. - MID is the largest, making it robust for generating information about drugs such as indications or interactions. - MID offers over 192k rows distributed in 44 variety therapeutic classes, making it robust for drug classification to therapeutic label. - MID provides accurate, authoritative, and trustworthy information on medicines for enhancing predictions and efficiencies in clinical trial management. - MID includes details such as drug names, information URL, salt composition, drug introduction, therapeutic uses, side effects, drug benefits, how to use of drug, how to use of drug, how drug works, quick tips of drug, safety advice of drug, chemical class of drug, habit forming of drug, therapeutic class of drug, and action class of drug. This dataset aims to provide a useful resource for medical researchers, healthcare professionals, drug manufacturers, data scientists, and enthusiasts interested in exploring the world of medicines and healthcare products. - In contrast with the few small available datasets, MID's size makes it a suitable corpus for implementing both classical as well as deep learning models. • MID.xlsx provides the raw data, including medicine information. The data collected to ensure an acceleration and save experimental efforts for medicines through help in predicting or generating or classifying of medicine information preclinically. • Therapeutic_class_counts.xlsx is summarize distribution of medicines per therapeutic class.