OOMMPPAA: a tool to aid directed synthesis by the combined analysis of activity and structural data.

Anthony R Bradley,Charlotte M Deane,Darren V S Green,Brian D Marsden,Ian D Wall

doi:10.1021/ci500245d

Anthony R Bradley, Charlotte M Deane + Show 3 more

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https://doi.org/10.1021/ci500245d

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Abstract

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/.

Highlights

In recent years approaches such as high-throughput crystallography and Fragment Based Drug Design have reached maturity, resulting in a rapidly increasing number of available crystal structures and more liganded structures for a given protein
The examples used are derived from cyclindependent kinase 2 (CDK2) and a S-adenosyl methionine (SAM)-dependent methyl-transferase
OOMMPPAA is a novel and freely available computational tool to aid in directed synthesis by analysis of large structural and activity data sets, comprising tens of liganded structures and hundreds of activity data points from Ki and IC50 data

Summary

Introduction

In recent years approaches such as high-throughput crystallography and Fragment Based Drug Design have reached maturity, resulting in a rapidly increasing number of available crystal structures and more liganded structures for a given protein. Improvements in small-molecule screening throughput and initiatives to consolidate activity data from disparate sources have made thousands of high-quality small-molecule activity data points available for many biologically important protein targets both in the public domain[1] and within the pharmaceutical industry. This data is a key resource in the early stages of drug discovery as it provides information that may aid in the design of smallmolecules as part of lead-discovery and lead-optimization.[2,3] despite the availability of this wealth of new data, there are few computational tools that are able to systematically exploit it. There is a clear need for novel automated methods that can use these data sets to assist medicinal and computational chemists in the directed synthesis of smallmolecules

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