Name
Abstract | ||
|---|---|---|
Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s10822-006-9096-5 | Journal of computer-aided molecular design |
Keywords | DocType | Volume |
Group fusion,Kernel discrimination,Ligand-based virtual screening,Machine learning,Naive Bayesian classifier,Similarity searching,Virtual screening | Journal | 21 |
Issue | ISSN | Citations |
1-3 | 0920-654X | 25 |
PageRank | References | Authors |
0.99 | 25 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beining Chen | 1 | 79 | 6.27 |
| Robert F. Harrison | 2 | 277 | 29.20 |
| George Papadatos | 3 | 325 | 16.97 |
| PETER WILLETT | 4 | 3421 | 592.93 |
| David J Wood | 5 | 65 | 3.88 |
| Xiao Qing Lewell | 6 | 77 | 7.72 |
| Paulette Greenidge | 7 | 25 | 0.99 |
| Nikolaus Stiefl | 8 | 57 | 12.48 |