Chemistry42: An AI-Driven Platform for Molecular Design and Optimization
Тип публикации: Journal Article
Дата публикации: 2023-02-02
scimago Q1
Tоп 10% SciMago
wos Q1
white level БС1
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
Краткое описание
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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Метрики
165
Всего цитирований:
165
Цитирований c 2025:
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(57.57%)
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MLA
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ГОСТ
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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.
ГОСТ со всеми авторами (до 50)
Скопировать
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.
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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 -
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BibTex (до 50 авторов)
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@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}
}
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MLA
Скопировать
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.
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