Linked Life Data

19398207

Source:http://linkedlifedata.com/resource/pubmed/id/19398207

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2009-5-6
pubmed:abstractText
The work described here is aimed at developing QSAR models capable of predicting in vitro human plasma lability/stability. They were built based on a dataset comprising about 200 known compounds. 3D structures of the molecules were drawn, optimized and submitted to the calculation of molecular descriptors that enabled selecting different TR/TS set pairs, subsequently exploited to develop QSAR models. Several 'machine learning' algorithms were explored in order to obtain suitable classification models, which were then validated on the relevant TS sets. Moreover the predictive ability of the best performing models was assessed on a Prediction set (PS) comprising about 40 molecules, not strictly related, from a structural point of view, to the initial dataset, but (obviously) comprised within the validity domain of the QSAR models obtained. The study allowed selecting predictive models enabling the classification of New Chemical Entities with regard to hydrolysis rate, that may be exploited for soft-drug design.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1464-3391
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3543-56
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
QSAR models for predicting enzymatic hydrolysis of new chemical entities in 'soft-drug' design.
pubmed:affiliation
Department of Chemistry and Industrial Chemistry, University of Pisa, Via Risorgimento 35, 56126 Pisa, Italy.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't