Practical notes on building molecular graph generative models

Applied AI Letters

Hongming Chen¹  Günter Klambauer²  Tobias Rastemo³  Edvard Lindelöf³  Rocío Mercado³  Ola Engkvist³  Esben Jannik Bjerrum³ 

[1] Centre of Chemistry and Chemical Biology, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory Guangzhou China;Institute of Bioinformatics, Johannes Kepler University Linz Austria;Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden;

关键词: code development;    code optimization;    deep learning;    drug discovery;    generative models;    graph neural networks;   

DOI  :  10.1002/ail2.18

来源: DOAJ

【 摘 要 】

Abstract Here are presented technical notes and tips on developing graph generative models for molecular design. Although this work stems from the development of GraphINVENT, a Python platform for iterative molecular generation using graph neural networks, this work is relevant to researchers studying other architectures for graph‐based molecular design. In this work, technical details that could be of interest to researchers developing their own molecular generative models are discussed, including an overview of previous work in graph‐based molecular design and strategies for designing new models. Advice on development and debugging tools which are helpful during code development is also provided. Finally, methods that were tested but which ultimately did not lead to promising results in the development of GraphINVENT are described here in the hope that this will help other researchers avoid pitfalls in development and instead focus their efforts on more promising strategies for graph‐based molecular generation.

【 授权许可】

Unknown