Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling.

Ligand profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects and drug repurposing. A few encouraging successes have already been reported using ligand 2-D similarity searches and protein-ligand docking. The current study describes the use of receptor-ligand-derived pharmacophore searches as a tool to link ligands to putative targets.

New features that improve the pharmacophore tools from Accelrys.

Generating a pharmacophore is often the first step towards understanding the interactions between a receptor and a ligand and can be pivotal to a successful drug discovery project. The pharmacophore tools at Accelrys have been used to assist in many different projects over the years, such as lead generation, scaffold hopping, mining ligand databases as well as many more. In this article, we will review the pharmacophore tools that have been developed at Accelrys.

CAESAR: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration.

A highly efficient conformer search algorithm based on a divide-and-conquer and recursive conformer build-up approach is presented in this paper. This approach is combined with consideration of local rotational symmetry so that conformer duplicates due to topological symmetry in the systematic search can be efficiently eliminated. This new algorithm, termed CAESAR (Conformer Algorithm based on Energy Screening and Recursive Buildup), has been implemented in Discovery Studio 1.

Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR.

This study provides results from two case studies involving the application of the HypoGenRefine algorithm within Catalyst for the automated generation of excluded volume from ligand information alone. A limitation of pharmacophore feature hypothesis alone is that activity prediction is based purely on the presence and arrangement of pharmacophoric features; steric effects remained unaccounted. Recently reported studies have illustrated the usefulness of combining excluded volumes to the pharmacophore models.