An online database of ML-generated molecules

Rustam Zhumagambetov, Daniyar Kazbek, Mansur Shakipov, Daulet Maksut, Vsevolod A. Peshkov, Siamac Fazli

Research output: Contribution to journalArticlepeer-review


Several recent ML algorithms for de novo molecule generation have been utilized to create an open-access database of virtual molecules. The algorithms were trained on samples from ZINC, a free database of commercially available compounds. Generated molecules, stemming from 10 different ML frameworks, along with their calculated properties were merged into a database and coupled to a web interface, which allows users to browse the data in a user friendly and convenient manner. ML-generated molecules with desired structures and properties can be retrieved with the help of a drawing widget. For the case of a specific search leading to insufficient results, users are able to create new molecules on demand. These newly created molecules will be added to the existing database and as a result, the content as well as the diversity of the database keeps growing in line with the user's requirements.

Original languageEnglish
Pages (from-to)45189-45198
Number of pages10
JournalRSC Advances
Issue number73
Publication statusPublished - Dec 11 2020

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


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