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There is an ever increasing resource in terms of both structural information and activity data for many protein targets. In this paper we describe OOMMPPAA, a novel computational tool designed to inform compound design by combining such data. OOMMPPAA uses 3D matched molecular pairs to generate 3D ligand conformations. It then identifies pharmacophoric transformations between pairs of compounds and associates them with their relevant activity changes. OOMMPPAA presents this data in an interactive application providing the user with a visual summary of important interaction regions in the context of the binding site. We present validation of the tool using openly available data for CDK2 and a GlaxoSmithKline data set for a SAM-dependent methyl-transferase. We demonstrate OOMMPPAA's application in optimizing both potency and cell permeability and use OOMMPPAA to highlight nuanced and cross-series SAR. OOMMPPAA is freely available to download at http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/ .

Original publication

DOI

10.1021/ci500245d

Type

Journal article

Journal

Journal of chemical information and modeling

Publication Date

09/10/2014

Volume

54

Pages

2636 - 2646

Addresses

SGC, Nuffield Department of Medicine, University of Oxford , Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.

Keywords

Humans, Methyltransferases, S-Adenosylmethionine, Enzyme Inhibitors, Ligands, Binding Sites, Protein Binding, Quantitative Structure-Activity Relationship, Drug Design, Software, Cyclin-Dependent Kinase 2, Small Molecule Libraries, Molecular Docking Simulation