Source:http://linkedlifedata.com/resource/pubmed/id/12115811
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
8
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| pubmed:dateCreated |
2002-7-12
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| pubmed:abstractText |
A quantitative structure-property relationship (QSPR) was developed for predicting the aqueous solubility of drug-like compounds from their chemical structures. A set of 321 structurally diverse drugs or related compounds, with their intrinsic aqueous solubility collected from literature, was used in this analysis. The data were divided into a training set (n = 267) for building the model and a randomly chosen testing set (n = 54) for assessing the predictive ability of the model. A series of molecular descriptors was calculated directly from chemical structures and a set of eight descriptors, including dipole moment, surface area, volume, molecular weight, number of rotatable bonds/total bonds, number of hydrogen-bond acceptors, number of hydrogen-bond donors and density, was chosen for the final model. The eight-descriptor model generated by multiple linear regression was further optimized by a genetic algorithm guided selection method. The model has a correlation coefficient (r) of 0.95 and a root-mean-square (rms) error of 0.56 log unit. It predicts the solubility of testing set compounds with a reasonable degree of accuracy (r = 0.84 and rms = 0.86 log unit). The present model can serve as a tool for medicinal chemists to guide their early synthetic efforts in arriving at appropriate analogs.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:chemical | |
| pubmed:status |
MEDLINE
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| pubmed:month |
Aug
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| pubmed:issn |
0022-3549
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| pubmed:author | |
| pubmed:copyrightInfo |
Copyright 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association
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| pubmed:issnType |
Print
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| pubmed:volume |
91
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
1838-52
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| pubmed:dateRevised |
2008-11-21
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| pubmed:meshHeading |
pubmed-meshheading:12115811-Algorithms,
pubmed-meshheading:12115811-Artificial Intelligence,
pubmed-meshheading:12115811-Chemistry, Physical,
pubmed-meshheading:12115811-Models, Molecular,
pubmed-meshheading:12115811-Organic Chemicals,
pubmed-meshheading:12115811-Physicochemical Phenomena,
pubmed-meshheading:12115811-Predictive Value of Tests,
pubmed-meshheading:12115811-Quantitative Structure-Activity Relationship,
pubmed-meshheading:12115811-Reproducibility of Results,
pubmed-meshheading:12115811-Solubility,
pubmed-meshheading:12115811-Solvents,
pubmed-meshheading:12115811-Water
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| pubmed:year |
2002
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| pubmed:articleTitle |
Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship.
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| pubmed:affiliation |
Discovery Pharmaceutics, L12-09, Preclinical Candidate Optimization, Bristol-Myers Squibb Pharmaceutical Research Institute, Lawrenceville, New Jersey 08543, USA.
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| pubmed:publicationType |
Journal Article
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