Linked Life Data

19323655

Source:http://linkedlifedata.com/resource/pubmed/id/19323655

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2009-4-15
pubmed:abstractText
Small molecules often have toxicities that are a function of molecular structural features. Minor variations in structural features can make large difference in such toxicity. Consequently, in silico techniques may be used to correlate such molecular toxicities with their structural features. Relative to nine different sets of aromatic nitro compounds having known observed toxicities against different targets, we developed ligand-based 2D quantitative structure-toxicity relationship models using 20 selected topological descriptors. The topological descriptors have several advantages such as conformational independency, facile and less time-consuming computation to yield good results. Multiple linear regression analysis was used to correlate variations of toxicity with molecular properties. The information index on molecular size, lopping centric index and Kier flexibility index were identified as fundamental descriptors for different kinds of toxicity, and further showed that molecular size, branching and molecular flexibility might be particularly important factors in quantitative structure-toxicity relationship analysis. This study revealed that topological descriptor-guided quantitative structure-toxicity relationship provided a very useful, cost and time-efficient, in silico tool for describing small-molecule toxicities.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1747-0285
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
73
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
537-44
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
In silico quantitative structure-toxicity relationship study of aromatic nitro compounds.
pubmed:affiliation
Computational Science Center, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Korea.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't