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 |
|---|---|
| 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