Collective recognition strategy for estimating hepatoprotector activity of various chemical compounds in increasing liver repair potential

Autor:	L. A. Radkevich, A. S. Kabankin
Rok vydání:	2013
Předmět:	Pharmacology Toxicology Training set Chemistry business.industry education Drug Discovery Pharmacology toxicology Liver repair Pattern recognition Artificial intelligence business Classifier (UML)
Zdroj:	Pharmaceutical Chemistry Journal. 47:181-186
ISSN:	1573-9031 0091-150X
DOI:	10.1007/s11094-013-0922-5
Popis:	The relationship between molecular characteristics and hepatoprotector activity (increasing liver repair potential) was investigated for a diverse series of chemical compounds. Application of the nearest-neighbor method using geometric descriptors to a training set including 44 compounds (23 active and 21 inactive) gave combinations of the descriptors that classified correctly 79 – 91% of the training-set compounds. It was shown that a classifier that classified correctly all training-set compounds could be constructed by applying a simple voting procedure to results obtained for various combinations of descriptors. The effectiveness of the proposed approach for predicting the activity of untested compounds was proved by cross-validation with groups of compounds removed from the training set.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e83dddd54f0da92c984e41a7036e39a3 https://doi.org/10.1007/s11094-013-0922-5 Zobrazit plný text záznamu Plný text ve formátu PDF