Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2002-5-10
pubmed:abstractText
Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available through molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late development failures. Quantitative structure-activity relationship (QSAR) methods have much to offer in these areas. However, QSAR analysis has many traps for unwary practitioners. This review introduces the concepts behind QSAR, points out problems that may be encountered, suggests ways of avoiding the pitfalls and introduces several exciting, new QSAR methods discovered during the last decade.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1467-5463
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
73-86
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
The role of quantitative structure--activity relationships (QSAR) in biomolecular discovery.
pubmed:affiliation
CSIRO Molecular Science, Clayton South MDC, Australia. Dave.winkler@molsci.csiro.au
pubmed:publicationType
Journal Article