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“In Silico” Design of New Uranyl Extractants Based on Phosphoryl-Containing Podands: QSPR Studies, Generation and Screening of Virtual Combinatorial Library, and Experimental Testsстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 3 декабря 2017 г.
- Авторы: Varnek A., Fourches D., Solov'ev V.P., Baulin V.E., Turanov A.N., Karandashev V.K., Fara D., Katritzky A.R.
- Журнал: Journal of chemical information and computer sciences
- Том: 44
- Номер: 4
- Год издания: 2004
- Издательство: American Chemical Society
- Местоположение издательства: United States
- Первая страница: 1365
- Последняя страница: 1382
- DOI: 10.1021/ci049976b
- Аннотация: This paper is devoted to computer-aided design of new extractants of the uranyl cation involving three main steps: (i) a QSPR study, (ii) generation and screening of a virtual combinatorial library, and (iii) synthesis of several predicted compounds and their experimental extraction studies. First, we performed a QSPR modeling of the distribution coefficient (logD) of uranyl extracted by phosphoryl-containing podands from water to 1,2-dichloroethane. Two different approaches were used: one based on classical structural and physicochemical descriptors (implemented in the CODESSA PRO program) and another one based on fragment descriptors (implemented in the TRAIL program). Three statistically significant models obtained with TRAIL involve as descriptors either sequences of atoms and bonds or atoms with their close environment (augmented atoms). The best models of CODESSA PRO include its own molecular descriptors as well as fragment descriptors obtained with TRAIL. At the second step, a virtual combinatorial library of 2024 podands has been generated with the CombiLib program, followed by the assessment of logD values using developed QSPR models. At the third step, eight of these hypothetical compounds were synthesized and tested experimentally. Comparison with experiment shows that developed QSPR models successfully predict logD values for 7 of 8 compounds from that “blind test” set.
- Добавил в систему: Соловьев Виталий Петрович