论文标题
深图卷积网络和基于LSTM的方法用于预测药物目标结合亲和力
Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity
论文作者
论文摘要
新药的开发是一个昂贵且耗时的过程。由于全球SARS-COV-2爆发，必须尽快开发SARS-COV-2的新药。药物重新利用的技术可以通过探测现有的FDA批准药物及其特性来减少开发新药物所需的时间范围，以重用它们来打击新疾病。我们提出了一种新型的架构DEEPGLSTM，它是基于图形卷积网络和基于LSTM的方法，可预测FDA批准的药物与SARS-COV-2的病毒蛋白之间的结合亲和力值。我们提出的模型已在戴维斯（Davis），Kiba（激酶抑制剂生物活性），DTC（药物目标共享），Metz，Toxcast和Stitch数据集进行了培训。我们使用新颖的结构来预测2,304种FDA批准的药物对5种病毒蛋白的合并得分（使用戴维斯和KIBA评分计算）。在总分的基础上，我们准备了对SARS-COV-2中5种病毒蛋白具有最高结合亲和力的前18名药物列表。随后，此列表可用于创建新的有用药物。
Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them for combating the new disease. We propose a novel architecture DeepGLSTM, which is a Graph Convolutional network and LSTM based method that predicts binding affinity values between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Our proposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity), DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novel architecture to predict a Combined Score (calculated using Davis and KIBA score) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis of the Combined Score, we prepare a list of the top-18 drugs with the highest binding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, this list may be used for the creation of new useful drugs.
