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-points pharmacophores present in the 3D structures of molecul es, 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 approa ches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of mol ecules for the detection of inhibitors of several drug targets.
@article{arxiv.q-bio/0603006,
title = {The pharmacophore kernel for virtual screening with support vector machines},
author = {Pierre Mahé and Liva Ralaivola and Véronique Stoven and Jean-Philippe Vert},
journal= {arXiv preprint arXiv:q-bio/0603006},
year = {2016}
}