Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.
【저자키워드】 Virtual drug screening, Predictive medicine, 【초록키워드】 COVID-19, clinical trial, Diseases, clinical trials, Drug screening, drug, outbreak, transcriptomic data, methodology, application, disease, gene co-expression network, Clinical use, first position, specified disease, candidate, Computational drug, detect, 【제목키워드】 COVID-19, Cancer, drug,