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Alexander V. Dmitriev

dblp:142/9571 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
0000-0002-2431-3429ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › drug discovery
drug metabolism prediction		 0.212015
SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds · Bioinform. 2015
Bioinformatics and computational biology › drug discovery › drug metabolism prediction
site of metabolism prediction		 0.212015
SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds · Bioinform. 2015

Methods — techniques the papers use, named apart from their topics

multilevel neighbourhoods of atoms descriptors · 0.2bayesian approach · 0.2
YearPublicationVenuePosition
2015 SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds
abstract
UNLABELLED: A new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of 'structure-SOM' relationships using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average invariant accuracy of prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.way2drug.com/SOMP.
Anastasia V. Rudik, Alexander V. Dmitriev, Alexey Lagunin, Dmitry Filimonov, Vladimir Poroikov
 Bioinform.2