Deep Learning Insights into Lanthanides Complexation Chemistry |
| |
Authors: | Artem A Mitrofanov Petr I Matveev Kristina V Yakubova Alexandru Korotcov Boris Sattarov Valery Tkachenko Stepan N Kalmykov |
| |
Institution: | 1.Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory, 1 bld.3, 119991 Moscow, Russia; (P.I.M.); (S.N.K.);2.Science Data Software, 14909 Forest Landing Cir, Rockville, MD 20850, USA; (K.V.Y.); (A.K.); (B.S.); (V.T.) |
| |
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. |
| |
Keywords: | lanthanides deep learning complexation |
|
|