Descriptors you can count on? Normalized and filtered pharmacophore descriptors for virtual screening

The three-dimensional (3D) binary pharmacophore fingerprints find wide application as descriptors in applications ranging from virtual screening through library design. While the 3D content they capture is an intuitively attractive feature of such measures, maximizing their signal to noise ratio has proven to be a tricky balancing act. This issue surfaces primarily due to the potential of such fingerprints to create an explosion of pharmacophores as molecular complexity and flexibility increases. In this article, we describe a modification to the fingerprint generation process that normalizes pharmacophore occurrence frequency by the conformational ensemble size used to derive the descriptor. By including pharmacophore frequency and conformational count, the importance of a given pharmacophore is weighted by the probability of its existence within a given conformational ensemble, rather than treating each pharmacophore equally. In addition, a number of filters have been added to permit the removal of unwanted pharmacophores from the descriptor set. These filters are based on pharmacophore composition (e.g. permutations made up primarily of lipophilic and/or aromatic centers), and size (pharmacophore perimeter length relative to the largest perimeter length found in the molecule). The highly uneven nature of pharmacophore distributions across the conformational ensemble used to generate them is highlighted, as are enrichment comparisons with their binary fingerprint peers. In addition, the limitations in descriptor comparison validation are high-lighted as an illustration of the need for more extensive validation experiments.