Chemistry42: An AI-Driven Platform for Molecular Design and Optimization
Yan Ivanenkov
1
,
Dmitry Bezrukov
1
,
Dmitry S. Bezrukov
1
,
Bogdan Zagribelnyy
3
,
Vladimir Aladinskiy
3
,
Vladimir A. Aladinskiy
3
,
Petrina Kamya
2
,
Alex Aliper
3
,
Feng Ren
4
,
1
Insilico Medicine Kong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok, Hong Kong
|
2
Insilico Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, H3B 4W8 Canada
|
3
Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, PO Box 145748, Abu Dhabi, UAE
|
4
Insilico Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
|
Publication type: Journal Article
Publication date: 2023-02-02
scimago Q1
wos Q1
SJR: 1.467
CiteScore: 9.8
Impact factor: 5.3
ISSN: 15499596, 1549960X
PubMed ID:
36728505
General Chemistry
Computer Science Applications
General Chemical Engineering
Library and Information Sciences
Abstract
Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20.
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Metrics
140
Total citations:
140
Citations from 2024:
121
(86.43%)
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GOST
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Ivanenkov Y. et al. Chemistry42: An AI-Driven Platform for Molecular Design and Optimization // Journal of Chemical Information and Modeling. 2023. Vol. 63. No. 3. pp. 695-701.
GOST all authors (up to 50)
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Ivanenkov Y., Polykovskiy D., Bezrukov D., Bezrukov D. S., Zagribelnyy B., Aladinskiy V., A. Aladinskiy V., Kamya P., Aliper A., Ren F., Zhavoronkov A. Chemistry42: An AI-Driven Platform for Molecular Design and Optimization // Journal of Chemical Information and Modeling. 2023. Vol. 63. No. 3. pp. 695-701.
Cite this
RIS
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TY - JOUR
DO - 10.1021/acs.jcim.2c01191
UR - https://pubs.acs.org/doi/10.1021/acs.jcim.2c01191
TI - Chemistry42: An AI-Driven Platform for Molecular Design and Optimization
T2 - Journal of Chemical Information and Modeling
AU - Ivanenkov, Yan
AU - Polykovskiy, Daniil
AU - Bezrukov, Dmitry
AU - Bezrukov, Dmitry S.
AU - Zagribelnyy, Bogdan
AU - Aladinskiy, Vladimir
AU - A. Aladinskiy, Vladimir
AU - Kamya, Petrina
AU - Aliper, Alex
AU - Ren, Feng
AU - Zhavoronkov, Alex
PY - 2023
DA - 2023/02/02
PB - American Chemical Society (ACS)
SP - 695-701
IS - 3
VL - 63
PMID - 36728505
SN - 1549-9596
SN - 1549-960X
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2023_Ivanenkov,
author = {Yan Ivanenkov and Daniil Polykovskiy and Dmitry Bezrukov and Dmitry S. Bezrukov and Bogdan Zagribelnyy and Vladimir Aladinskiy and Vladimir A. Aladinskiy and Petrina Kamya and Alex Aliper and Feng Ren and Alex Zhavoronkov},
title = {Chemistry42: An AI-Driven Platform for Molecular Design and Optimization},
journal = {Journal of Chemical Information and Modeling},
year = {2023},
volume = {63},
publisher = {American Chemical Society (ACS)},
month = {feb},
url = {https://pubs.acs.org/doi/10.1021/acs.jcim.2c01191},
number = {3},
pages = {695--701},
doi = {10.1021/acs.jcim.2c01191}
}
Cite this
MLA
Copy
Ivanenkov, Yan, et al. “Chemistry42: An AI-Driven Platform for Molecular Design and Optimization.” Journal of Chemical Information and Modeling, vol. 63, no. 3, Feb. 2023, pp. 695-701. https://pubs.acs.org/doi/10.1021/acs.jcim.2c01191.