MecDDI: Clarified Drug–Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug–Drug Interaction Prediction

Abstract

Drug–drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled “MecDDI” was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: .

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

“…Herein, five methods (KWT, ANOVA, PLS-DA, RF-RFE, and SVM-RFE) for identifying metabolic markers are widely used in multiclass metabolomic studies. To evaluate these methods, the stability of different marker groups identified in different data sets using the same methods was considered. − First, one multiclass metabolomic data set was separated into three subsets by stratified sampling. Second, one specific method was used to identify the markers for each subset.…”

Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review

“…This material is provided for educational purposes. MecDDI can provide medical scientists with a clear picture of DDI mechanisms and prepare data for algorithmic scientists to predict new DDIs, with over 110,000 underlying DDI mechanisms illustrated by clear descriptions and graphics in this Web site. The frequently used databases are listed in Table along with brief summaries and their respective URLs.…”

SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement

“…DDI may enhance or weaken the efficacy of the drug, causing adverse drug reactions (ADRs), which can even be life-threatening in severe cases [2]. Some databases, such as DrugBank [3], TWOSIDES [4], DDInter [5], KEGG [6], BIOSNAP [7] and MecDDI [8] have been established to provide information related to interactions between drugs and promote the development of new drugs while avoiding ADRs. Currently, the rapid growth in the number of biomedical publications makes it increasingly difficult to manually extract valuable DDI information from articles, despite its critical importance [9].…”