TY - JOUR
T1 - CheML.io
T2 - An online database of ML-generated molecules
AU - Zhumagambetov, Rustam
AU - Kazbek, Daniyar
AU - Shakipov, Mansur
AU - Maksut, Daulet
AU - Peshkov, Vsevolod A.
AU - Fazli, Siamac
N1 - Funding Information:
This research was supported by Nazarbayev University Faculty Development Grant (240919FD3926). The authors would also like to acknowledge the support of NPO Young Researchers Alliance and Nazarbayev University Corporate Fund “Social Development Fund” for grant under their Fostering Research and Innovation Potential Program.
Publisher Copyright:
© The Royal Society of Chemistry.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/11
Y1 - 2020/12/11
N2 - 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.
AB - 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.
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U2 - 10.1039/d0ra07820d
DO - 10.1039/d0ra07820d
M3 - Article
AN - SCOPUS:85098213399
SN - 2046-2069
VL - 10
SP - 45189
EP - 45198
JO - RSC Advances
JF - RSC Advances
IS - 73
ER -