Flexsim-R: a virtual affinity fingerprint descriptor to calculate similarities of functional groups.

Abstract

Methods to describe the similarity of fragments occurring in drug-like molecules are of fundamental importance in computational drug design. In the early phase of lead discovery, they can help to select diverse building blocks for combinatorial compound libraries intended for broad screening. In lead optimization, such methods can guide bioisosteric replacements of one functional group by another or serve as descriptors for QSAR calculations. In this paper, we outline the development of a novel 3D descriptor, termed Flexsim-R, which is a further extension of our virtual affinity fingerprint idea. Descriptors are calculated based on docking of small fragments such as building blocks for combinatorial chemistry or functional groups of drug-like molecules into a reference panel of protein binding sites. The method is validated by examining the neighborhood behavior of the affinity fingerprints and by deriving predictive QSAR models for a couple of literature peptide data sets.