#6108. MGRNN: Structure Generation of Molecules Based on Graph Recurrent Neural Networks

October 2026publication date
Proposal available till 10-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Science Applications;
Organic Chemistry;
Drug Discovery;
Structural Biology;
Molecular Medicine;
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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.
Keywords:
Graph recurrent neural network; machine learning; molecular generation

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