Molecular generative Graph Neural Networks for Drug Discovery

NEUROCOMPUTING

卷:450

Article

Bongini, Pietro^1,2  Bianchini, Monica¹  Scarselli, Franco¹ 

[1] Univ Siena, Dept Informat Engn & Math, Via Roma 56, I-53100 Siena, Italy

[2] Univ Florence, Dept Informat Engn, Via Santa Marta 3, I-50139 Florence, Italy

关键词: Graph generation;    Molecule generation;    Deep learning;    Graph Neural Networks;    Drug Discovery;   

DOI  :  10.1016/j.neucom.2021.04.039

来源: Elsevier

【 摘 要 】

Drug Discovery is a fundamental and ever-evolving field of research. The design of new candidate molecules requires large amounts of time and money, and computational methods are being increasingly employed to cut these costs. Machine learning methods are ideal for the design of large amounts of potential new candidate molecules, which are naturally represented as graphs. Graph generation is being revolutionized by deep learning methods, and molecular generation is one of its most promising applications. In this paper, we introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG(2)N(2). At each step, a node or a group of nodes is added to the graph, along with its connections. The modular architecture simplifies the training procedure, also allowing an independent retraining of a single module. Sequentiality and modularity make the generation process interpretable. The use of Graph Neural Networks maximizes the information in input at each generative step, which consists of the subgraph produced during the previous steps. Experiments of unconditional generation on the QM9 and Zinc datasets show that our model is capable of generalizing molecular patterns seen during the training phase, without overfitting. The results indicate that our method is competitive, and outperforms challenging baselines for unconditional generation. (C) 2021 Elsevier B.V. All rights reserved.

【 授权许可】

Free