Rocío Mercado, T. Rastemo, Edvard Lindelöf, G. Klambauer, O. Engkvist, Hongming Chen, E. Bjerrum
{"title":"Practical notes on building molecular graph generative models","authors":"Rocío Mercado, T. Rastemo, Edvard Lindelöf, G. Klambauer, O. Engkvist, Hongming Chen, E. Bjerrum","doi":"10.26434/chemrxiv.12888383","DOIUrl":null,"url":null,"abstract":"Here are presented technical notes\nand tips on developing graph generative models for molecular design. This work\nstems from the development of GraphINVENT, a Python platform for graph-based molecular\ngeneration using graph neural networks. In this work, technical details that\ncould be of interest to researchers developing their own molecular generative\nmodels are discussed, including strategies for designing new models. Advice on development and debugging tools\nwhich were helpful during code development is also provided. Finally, methods that were tested but which ultimately\ndidn’t lead to promising results in the development of GraphINVENT are\ndescribed here in the hope that this will help other researchers avoid pitfalls\nin development and instead focus their efforts on more promising strategies for\ngraph-based molecular generation.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied AI letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26434/chemrxiv.12888383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
Here are presented technical notes
and tips on developing graph generative models for molecular design. This work
stems from the development of GraphINVENT, a Python platform for graph-based molecular
generation using graph neural networks. In this work, technical details that
could be of interest to researchers developing their own molecular generative
models are discussed, including strategies for designing new models. Advice on development and debugging tools
which were helpful during code development is also provided. Finally, methods that were tested but which ultimately
didn’t 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.