Lead Hopping Using SVM and 3D Pharmacophore Fingerprints

The combination of 3D pharmacophore fingerprints and the support vector machine classification algorithm
has been used to generate robust models that are able to classify compounds as active or inactive in a
number of G-protein-coupled receptor assays. The models have been tested against progressively more
challenging validation sets where steps are taken to ensure that compounds in the validation set are chemically
and structurally distinct from the training set. In the most challenging example, we simulate a lead-hopping
experiment by excluding an entire class of compounds (defined by a core substructure) from the training
set. The left-out active compounds comprised approximately 40% of the actives. The model trained on the
remaining compounds is able to recall 75% of the actives from the “new” lead series while correctly classifying
>99% of the 5000 inactives included in the validation set.