LigSeeSVM: Ligand-based virtual Screening using Support Vector Machines and data fusion

Yen Fu Chen, Kai Cheng Hsu, Po Tsun Lin, D. Frank Hsu, Bruce S. Kristal, Jinn-Moon Yang*

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Ligand-based in silico drug screening is useful for lead discovery, in particular for those targets without structures. Here, we have developed LigSeeSVM, a ligand-based screening tool using data fusion and Support Vector Machines (SVMs). We used Atom Pair (AP) structure descriptors and Physicochemical (PC) descriptors of compounds to generate SVM-AP and SVM-PC models. Sequentially, the two models were combined using rank-based data fusion to create LigSeeSVM model. LigSeeSVM was evaluated on five data sets. Experimental results show that the performance of LigSeeSVM is better than other ligand-based virtual screening approaches. We believe that LigSeeSVM is useful for lead compounds.

Original languageEnglish
Pages (from-to)274-289
Number of pages16
JournalInternational Journal of Computational Biology and Drug Design
Volume4
Issue number3
DOIs
StatePublished - 1 Jul 2011

Keywords

  • Data fusion
  • Ligand-based virtual screening
  • Oestrogen receptor
  • Rank combination
  • Support Vector Machines
  • Thymidine kinase substrates

Fingerprint Dive into the research topics of 'LigSeeSVM: Ligand-based virtual Screening using Support Vector Machines and data fusion'. Together they form a unique fingerprint.

  • Cite this