Name
Title | ||
|---|---|---|
LigSeeSVM: Ligand-based virtual Screening using Support Vector Machines and data fusion. |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1504/IJCBDD.2011.041415 | Int. J. Comput. Biol. Drug Des. |
Keywords | Field | DocType |
drug discovery,artificial intelligence,algorithms,data fusion,support vector machines,roc curve,computational biology | Combinatorial Chemistry Techniques,Data set,Drug discovery,Ligand,Support vector machine,Chemistry,Sensor fusion,Artificial intelligence,Bioinformatics,Virtual screening,Machine learning,In silico | Journal |
Volume | Issue | ISSN |
4 | 3 | 1756-0756 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yen-Fu Chen | 1 | 79 | 4.88 |
| Kai-Cheng Hsu | 2 | 82 | 8.27 |
| Po-Tsun Lin | 3 | 0 | 0.68 |
| D. Frank Hsu | 4 | 722 | 66.32 |
| Bruce S Kristal | 5 | 78 | 4.91 |
| Jinn-moon Yang | 6 | 364 | 35.89 |