Open Access
Molecules, volume 26, issue 11, pages 3237
Deep Learning Insights into Lanthanides Complexation Chemistry
Mitrofanov Artem A
1, 2
,
Matveev Petr I
1
,
Yakubova Kristina V
2
,
Korotcov Alexandru
2
,
Sattarov Boris
2
,
Tkachenko Valery
2
,
2
Science Data Software, 14909 Forest Landing Cir, Rockville, MD 20850, USA
|
Publication type: Journal Article
Publication date: 2021-05-27
PubMed ID:
34072262
Organic Chemistry
Drug Discovery
Physical and Theoretical Chemistry
Pharmaceutical Science
Molecular Medicine
Analytical Chemistry
Chemistry (miscellaneous)
Abstract
Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. We have a series of chemically similar lanthanide ions, and we have collected data on complexes’ stability, built models, predicting stability constants and decoded the models to obtain key fragments responsible for complexation efficiency. The results are in good correlation with the experimental ones, as well as modern theories of complexation. It was shown that the main influence on the constants had a mutual location of the binding centers.
Citations by journals
|
1
|
|
|
Inorganic Chemistry Frontiers
|
Inorganic Chemistry Frontiers
1 publication, 100%
|
|
1
|
Citations by publishers
|
1
|
|
|
Royal Society of Chemistry (RSC)
|
Royal Society of Chemistry (RSC)
1 publication, 100%
|
|
1
|
- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
{"yearsCitations":{"type":"bar","data":{"show":true,"labels":[2022],"ids":[0],"codes":[0],"imageUrls":[""],"datasets":[{"label":"Citations number","data":[1],"backgroundColor":["#3B82F6"],"percentage":["100"],"barThickness":null}]},"options":{"indexAxis":"x","maintainAspectRatio":true,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":1,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Citations per year","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"journals":{"type":"bar","data":{"show":true,"labels":["Inorganic Chemistry Frontiers"],"ids":[8397],"codes":[0],"imageUrls":["\/storage\/images\/resized\/leiAYcRDGTSl5B1eCnwpSGqmDEUEfDPPoYisFGhT_medium.webp"],"datasets":[{"label":"","data":[1],"backgroundColor":["#3B82F6"],"percentage":[100],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Journals","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"publishers":{"type":"bar","data":{"show":true,"labels":["Royal Society of Chemistry (RSC)"],"ids":[123],"codes":[0],"imageUrls":["\/storage\/images\/resized\/leiAYcRDGTSl5B1eCnwpSGqmDEUEfDPPoYisFGhT_medium.webp"],"datasets":[{"label":"","data":[1],"backgroundColor":["#3B82F6"],"percentage":[100],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Publishers","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}}}
Metrics
Cite this
GOST |
RIS |
BibTex |
MLA
Cite this
GOST
Copy
Mitrofanov A. A. et al. Deep Learning Insights into Lanthanides Complexation Chemistry // Molecules. 2021. Vol. 26. No. 11. p. 3237.
GOST all authors (up to 50)
Copy
Mitrofanov A. A., Matveev P. I., Yakubova K. V., Korotcov A., Sattarov B., Tkachenko V., Kalmykov S. N. Deep Learning Insights into Lanthanides Complexation Chemistry // Molecules. 2021. Vol. 26. No. 11. p. 3237.
Cite this
RIS
Copy
TY - JOUR
DO - 10.3390/molecules26113237
UR - https://doi.org/10.3390%2Fmolecules26113237
TI - Deep Learning Insights into Lanthanides Complexation Chemistry
T2 - Molecules
AU - Mitrofanov, Artem A
AU - Matveev, Petr I
AU - Yakubova, Kristina V
AU - Korotcov, Alexandru
AU - Sattarov, Boris
AU - Tkachenko, Valery
AU - Kalmykov, Stepan N.
PY - 2021
DA - 2021/05/27 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 3237
IS - 11
VL - 26
PMID - 34072262
SN - 1420-3049
ER -
Cite this
BibTex
Copy
@article{2021_Mitrofanov,
author = {Artem A Mitrofanov and Petr I Matveev and Kristina V Yakubova and Alexandru Korotcov and Boris Sattarov and Valery Tkachenko and Stepan N. Kalmykov},
title = {Deep Learning Insights into Lanthanides Complexation Chemistry},
journal = {Molecules},
year = {2021},
volume = {26},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {may},
url = {https://doi.org/10.3390%2Fmolecules26113237},
number = {11},
pages = {3237},
doi = {10.3390/molecules26113237}
}
Cite this
MLA
Copy
Mitrofanov, Artem A., et al. “Deep Learning Insights into Lanthanides Complexation Chemistry.” Molecules, vol. 26, no. 11, May. 2021, p. 3237. https://doi.org/10.3390%2Fmolecules26113237.