  We present a computational scheme for predicting the ligands that bind to a
pocket of known structure. It is based on the generation of a general abstract
representation of the molecules, which is invariant to rotations, translations
and permutations of atoms, and has some degree of isometry with the space of
conformations. We use these representations to train a non-deep machine
learning algorithm to classify the binding between pockets and molecule pairs,
and show that this approach has a better generalization capability than
existing methods.
