Cargando…
Deep Learning Insights into Lanthanides Complexation Chemistry
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...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198800/ https://www.ncbi.nlm.nih.gov/pubmed/34072262 http://dx.doi.org/10.3390/molecules26113237 |
_version_ | 1783707225036947456 |
---|---|
author | Mitrofanov, Artem A. Matveev, Petr I. Yakubova, Kristina V. Korotcov, Alexandru Sattarov, Boris Tkachenko, Valery Kalmykov, Stepan N. |
author_facet | Mitrofanov, Artem A. Matveev, Petr I. Yakubova, Kristina V. Korotcov, Alexandru Sattarov, Boris Tkachenko, Valery Kalmykov, Stepan N. |
author_sort | Mitrofanov, Artem A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8198800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81988002021-06-14 Deep Learning Insights into Lanthanides Complexation Chemistry Mitrofanov, Artem A. Matveev, Petr I. Yakubova, Kristina V. Korotcov, Alexandru Sattarov, Boris Tkachenko, Valery Kalmykov, Stepan N. Molecules Communication 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. MDPI 2021-05-27 /pmc/articles/PMC8198800/ /pubmed/34072262 http://dx.doi.org/10.3390/molecules26113237 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Mitrofanov, Artem A. Matveev, Petr I. Yakubova, Kristina V. Korotcov, Alexandru Sattarov, Boris Tkachenko, Valery Kalmykov, Stepan N. Deep Learning Insights into Lanthanides Complexation Chemistry |
title | Deep Learning Insights into Lanthanides Complexation Chemistry |
title_full | Deep Learning Insights into Lanthanides Complexation Chemistry |
title_fullStr | Deep Learning Insights into Lanthanides Complexation Chemistry |
title_full_unstemmed | Deep Learning Insights into Lanthanides Complexation Chemistry |
title_short | Deep Learning Insights into Lanthanides Complexation Chemistry |
title_sort | deep learning insights into lanthanides complexation chemistry |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198800/ https://www.ncbi.nlm.nih.gov/pubmed/34072262 http://dx.doi.org/10.3390/molecules26113237 |
work_keys_str_mv | AT mitrofanovartema deeplearninginsightsintolanthanidescomplexationchemistry AT matveevpetri deeplearninginsightsintolanthanidescomplexationchemistry AT yakubovakristinav deeplearninginsightsintolanthanidescomplexationchemistry AT korotcovalexandru deeplearninginsightsintolanthanidescomplexationchemistry AT sattarovboris deeplearninginsightsintolanthanidescomplexationchemistry AT tkachenkovalery deeplearninginsightsintolanthanidescomplexationchemistry AT kalmykovstepann deeplearninginsightsintolanthanidescomplexationchemistry |