Linked Life Data

20038188

Source:http://linkedlifedata.com/resource/pubmed/id/20038188

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-1-25
pubmed:abstractText
In this study, we developed a new pharmacophore-based interaction fingerprint (Pharm-IF) and examined its usefulness for in silico screening using machine learning techniques such as support vector machine (SVM) and random forest (RF) instead of similarity-based ranking. Using the docking results of PKA, SRC, cathepsin K, carbonic anhydrase II, and HIV-1 protease, the screening efficiencies of the Pharm-IF models were compared to GLIDE score and the residue-based IF (PLIF) models. The combination of SVM and Pharm-IF demonstrated a higher enrichment factor at 10% (5.7 on average) than those of GLIDE score (4.2) and PLIF (4.3). In terms of the size of the training sets, learning more than five crystal structures enabled the machine learning models to stably achieve better efficiencies than GLIDE score. We also employed the docking poses of known active compounds, in addition to the crystal structures, as positive samples of training sets. The enrichment factors of the RF models at 10% using the docking poses for SRC and cathepsin K showed significantly higher values (6.5 and 6.3) than those using only the crystal structures (3.9 and 3.2), respectively.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1549-960X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
170-85
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening.
pubmed:affiliation
Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
pubmed:publicationType
Journal Article