kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a predictio

Keywords: gcn
Date: 2020/06/30 03:51

Related Entries

Read more 【ICLR2020採択論文】GANのなめらかさと安定性 | Preferred Networks Research & Development
0 users, 24 mentions 2020/01/06 04:00
Read more Google Flu Trends Still Appears Sick: An Evaluation of the 2013-2014 Flu Season by David Lazer, Ryan...
0 users, 2 mentions 2020/04/07 20:21
Read more はじめての自然言語処理 続・T5 によるテキスト生成の検証 | オブジェクトの広場
0 users, 1 mentions 2020/06/15 23:21
Read more Natural Language Processing with Attention Models | CourseraListLoupe CopyLoupe CopyLoupe CopySharea...
0 users, 1 mentions 2020/06/21 03:52
Read more PyCon JP Blog: PyCon JP 2020 プロポーザル採択速報 / Proposal selection bulletin
0 users, 10 mentions 2020/07/04 14:22