PDF(Pubmed)
Abstract：
BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects.
RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of \"interaction-affinity-binding sites\". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN.
CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .
RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of \"interaction-affinity-binding sites\". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN.
CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .
摘要：
背景：准确识别药物-靶标相互作用（DTI），亲和力（DTA），结合位点（DTS）对药物筛选至关重要，重新定位,和设计，以及理解目标的功能。尽管有一些基于深度学习的在线平台用于药物-靶标相互作用，亲和力,和结合位点识别，目前没有针对所有三个方面的集成在线平台。
结果：我们的解决方案，新颖的集成在线平台Drug-Online，已经被开发来促进药物筛选，目标识别,并以“相互作用-亲和力结合位点”的渐进方式理解靶标的功能。药物在线平台由三部分组成:第一部分采用药物-靶标相互作用识别方法MTraphDTA,基于图神经网络(GNN)和卷积神经网络(CNN)，以确定是否存在药物-靶标相互作用。如果识别出交互，第二部分采用药物-靶标亲和力鉴定方法MMDTA，也基于GNN和CNN，计算药物-靶标相互作用的强度，即，亲和力。最后,第三部分确定药物-靶标结合位点，即，口袋。本部分使用的pt-lm-gnn方法也是基于GNN。
结论：药物在线是一个可靠的在线平台，整合了药物-靶标相互作用，亲和力,和结合位点识别。它可以通过互联网免费获得，网址为http://39.106.7.26:8000/Drug-Online/。
结果：我们的解决方案，新颖的集成在线平台Drug-Online，已经被开发来促进药物筛选，目标识别,并以“相互作用-亲和力结合位点”的渐进方式理解靶标的功能。药物在线平台由三部分组成:第一部分采用药物-靶标相互作用识别方法MTraphDTA,基于图神经网络(GNN)和卷积神经网络(CNN)，以确定是否存在药物-靶标相互作用。如果识别出交互，第二部分采用药物-靶标亲和力鉴定方法MMDTA，也基于GNN和CNN，计算药物-靶标相互作用的强度，即，亲和力。最后,第三部分确定药物-靶标结合位点，即，口袋。本部分使用的pt-lm-gnn方法也是基于GNN。
结论：药物在线是一个可靠的在线平台，整合了药物-靶标相互作用，亲和力,和结合位点识别。它可以通过互联网免费获得，网址为http://39.106.7.26:8000/Drug-Online/。
