Name
Abstract | ||
|---|---|---|
We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-point pharmacophores present in the 3D structures of molecules, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approaches. Experimental results suggest that this new approach is competitive with state-of-the-art algorithms based on the 2D structure of molecules for the detection of inhibitors of several drug targets. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1021/ci060138m | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
Keywords | Field | DocType |
positive definite kernel,drug targeting,quantitative method,support vector machine,kernel method,virtual screening | Kernel (linear algebra),Graph kernel,Pharmacophore,Support vector machine,Fingerprint,Artificial intelligence,Bioinformatics,Virtual screening,Kernel method,Mathematics,Cheminformatics,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 5 | 1549-9596 |
Citations | PageRank | References |
35 | 1.97 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre Mahé | 1 | 257 | 13.98 |
| Liva Ralaivola | 2 | 619 | 47.96 |
| Véronique Stoven | 3 | 254 | 8.99 |
| Jean-philippe Vert | 4 | 2754 | 158.52 |