A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor.

Han, L Y; Ma, X H; Lin, H H; Jia, J; Zhu, F; Xue, Y; Li, Z R; Cao, Z W; Ji, Z L; Chen, Y Z

Han, L Y; Ma, X H; Lin, H H; Jia, J; Zhu, F; Xue, Y; Li, Z R; Cao, Z W; Ji, Z L; Chen, Y Z.

Affiliation

Han LY; Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore.

J Mol Graph Model ; 26(8): 1276-86, 2008 Jun.

Article in En | MEDLINE | ID: mdl-18218332

Subject(s)

Artificial Intelligence; Drug Design; Quantitative Structure-Activity Relationship; Central Nervous System Agents/chemistry; Chemical Phenomena; Chemistry, Physical; Dopamine Antagonists/chemistry; Folic Acid Antagonists/chemistry; HIV Protease Inhibitors/chemistry; Hydrophobic and Hydrophilic Interactions; Molecular Structure

Fulltext

XML

PubMed Links

Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Drug Design / Quantitative Structure-Activity Relationship Type of study: Diagnostic_studies / Prognostic_studies / Screening_studies Language: En Journal: J Mol Graph Model Journal subject: BIOLOGIA MOLECULAR Year: 2008 Type: Article Affiliation country: Singapore

Fulltext

XML

PubMed Links

Search on Google