Practical notes on building molecular graph generative models
Other text in scientific journal, 2020

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.

graph neural networks

code development

drug discovery

deep learning

code optimization

molecular design

generative models

Author

Rocio Mercado

AstraZeneca AB

Tobias Rastemo

AstraZeneca AB

Student at Chalmers

Edvard Lindelof

AstraZeneca AB

Student at Chalmers

Gunter Klambauer

Johannes Kepler University of Linz (JKU)

Ola Engkvist

AstraZeneca AB

Hongming Chen

Bio Island Laboratory

Esben Jannik Bjerrum

AstraZeneca AB

Applied AI Letters

26895595 (eISSN)

Vol. 1 2 AIL218

Subject Categories

Bioinformatics (Computational Biology)

Software Engineering

DOI

10.1002/ail2.18

More information

Latest update

12/22/2023