MGRNN: Structure Generation of Molecules Based on Graph Recurrent Neural Networks. Issue 10 (19th August 2021)

• Record Type: Journal Article
• Authors: Lai, Xin; Yang, Peisong; Wang, Kunfeng; Yang, Qingyuan; Yu, Duli
• Is Part Of: Molecular informatics. Volume 40:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-19
• Subjects: Graph recurrent neural network -- molecular generation -- machine learning; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202100091
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: Molecular structure generation is a critical problem for materials science and has attracted growing attention. The problem is challenging since it requires to generate chemically valid molecular structures. Inspired by the recent work in deep generative models, we propose a graph recurrent neural network model for drug molecular structure generation, briefly called MGRNN (Molecular Graph Recurrent Neural Networks). MGRNN combines the advantages of both iterative molecular generation algorithm and the efficiency of the training strategies. Moreover, MGRNN shows: (i) efficient computation for training; (ii) high model robustness for data; and (iii) an iterative sampling process, which allows to use chemical domain expertise for valency checking. Experimental results show that MGRNN is able to generate 69 % chemically valid molecules even without chemical knowledge and 100 % valid molecules with chemical rules. Abstract :