title: Shape-based virtual screening with volumetric aligned molecular shapes.
abstract: Shape-based virtual screening is an established and effective method for identifying small molecules that are similar in shape and function to a reference ligand. We describe a new method of shape-based virtual screening, volumetric aligned molecular shapes (VAMS). VAMS uses efficient data structures to encode and search molecular shapes. We demonstrate that VAMS is an effective method for shape-based virtual screening and that it can be successfully used as a prefilter to accelerate more computationally demanding search algorithms. Unique to VAMS is a novel minimum/maximum shape constraint query for precisely specifying the desired molecular shape. Shape constraint searches in VAMS are particularly efficient and millions of shapes can be searched in a fraction of a second. We compare the performance of VAMS with two other shape-based virtual screening algorithms a benchmark of 102 protein targets consisting of more than 32 million molecular shapes and find that VAMS provides a competitive trade-off between run-time performance and virtual screening performance.
authors: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors: David Ryan
authors: DR
authors: Koes
authors: Carlos J
authors: CJ
authors: Camacho
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D008958:Models, Molecular; D015394:Molecular Structure; D013997:Time Factors
chemical_list: D008024:Ligands
keywords: GSS tree; molecular shape; shape constraints; shape indexing; virtual screening