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 language | English |
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Pages (from-to) | 274-289 |
Number of pages | 16 |
Journal | International Journal of Computational Biology and Drug Design |
Volume | 4 |
Issue number | 3 |
DOIs | |
Publication status | Published - Jul 2011 |
Externally published | Yes |
Keywords
- Data fusion
- Ligand-based virtual screening
- Oestrogen receptor
- Rank combination
- Support Vector Machines
- Thymidine kinase substrates
ASJC Scopus subject areas
- Drug Discovery
- Computer Science Applications