  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.
