文章基本信息

标题：Deep learning identifies synergistic drug combinations for treating COVID-19
本地全文：下载
作者：Wengong Jin ; Jonathan M. Stokes ; Richard T. Eastman 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2021
卷号：118
期号：39
DOI：10.1073/pnas.2105070118
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies that can mitigate the effect of the disease. While searching for individual drugs for this purpose has been met with difficulties, synergistic drug combinations offer a promising alternative. However, the lack of high-quality training data pertaining to drug combinations makes it challenging to use existing machine learning methods for effective novel combination prediction tasks. Our proposed approach addresses this challenge by leveraging additional readily available data, such as drug−target interactions, thus enabling an effective in silico search for synergistic combinations against SARS-CoV-2. Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.
关键词：endeep learning;drug discovery;drug synergy;SARS-CoV-2