Ajou University repository

Benchmarking Deep Graph Models for Large Molecular Generation
Citations

SCOPUS

0

Citation Export

Publication Year
2022-01-01
Journal
Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, pp.114-120
Mesh Keyword
Developed modelDevelopment processDrug developmentDrug discoveryGeneration methodGenerative modelGraph modelNovel moleculesSmall moleculesSynthesizability
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern RecognitionInformation Systems and ManagementHealth Informatics
Abstract
Which molecular generation method is best for large molecular generations? Finding a good lead molecule is an important task in drug discovery. Recently several deep graph generative models have been developed for generating novel molecules that can be further tested for synthesizability in the drug development process. Most of the developed models are trained on small molecules with a maximum length of thirty. However, there is a need for the generation of larger molecules. We tested six recently proposed graph neural network-based molecular generation methods on their large molecular generation performance using two datasets from the LigandBox database, which contain larger molecules than typically used ZINC250k and QM9 datasets. We use twelve evaluation measures to evaluate the quality of the generated molecules, including stability measures such as logP values and QEDs.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36789
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127541424&origin=inward
DOI
https://doi.org/10.1109/bigcomp54360.2022.00032
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9736461
Type
Conference
Funding
This work was supported in part by the National Research Foundation of Korea grant funded by the Korean government (2018R1A5A1060031). (Corresponding author: Lee Sael.)
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Lee, Sael Image
Lee, Sael이슬
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.