Better compounds faster: the development and exploitation of a desktop predictive chemistry toolkit.

Today's drug designer has access to vast quantities of data and an impressive array of sophisticated computational methods. At the same time, there is increasing pressure on the pharmaceutical industry to improve its productivity and reduce candidate drug attrition. We set out to develop a highly integrated suite of design and data analysis tools underpinned by the best predictive chemistry methods and models, with the aim of enabling multi-disciplinary compound design teams to make better informed design decisions. In this article we address the challenges of developing a powerful, flexible and user-friendly toolkit, and of maximising its exploitation by the design community. We describe the impact the toolkit has had on drug discovery projects and give our perspective on the future direction of this activity.

[1]  Nidhal Selmi,et al.  Making medicinal chemistry more effective--application of Lean Sigma to improve processes, speed and quality. , 2009, Drug discovery today.

[2]  Sean Ekins,et al.  Mobile apps for chemistry in the world of drug discovery. , 2011, Drug discovery today.

[3]  Michael J. Waring,et al.  Matrix-based multiparameter optimisation of glucokinase activators: the discovery of AZD1092 , 2011 .

[4]  W. Delano The case for open-source software in drug discovery. , 2005, Drug discovery today.

[5]  Xiang Yao,et al.  Advanced Biological and Chemical Discovery (ABCD): Centralizing Discovery Knowledge in an Inherently Decentralized World , 2007, J. Chem. Inf. Model..

[6]  Paul D. Leeson,et al.  The influence of the 'organizational factor' on compound quality in drug discovery , 2011, Nature Reviews Drug Discovery.

[7]  Daniel J. Warner,et al.  WizePairZ: A Novel Algorithm to Identify, Encode, and Exploit Matched Molecular Pairs with Unspecified Cores in Medicinal Chemistry , 2010, J. Chem. Inf. Model..

[8]  Christopher S. Poss,et al.  Project-Focused Activity and Knowledge Tracker: A Unified Data Analysis, Collaboration, and Workflow Tool for Medicinal Chemistry Project Teams , 2009, J. Chem. Inf. Model..

[9]  Egon L. Willighagen,et al.  The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics , 2003, J. Chem. Inf. Comput. Sci..

[10]  John Clark,et al.  PGVL Hub: An integrated desktop tool for medicinal chemists to streamline design and synthesis of chemical libraries and singleton compounds. , 2011, Methods in molecular biology.

[11]  Chris Davies,et al.  The design and synthesis of novel N-hydroxyformamide inhibitors of ADAM-TS4 for the treatment of osteoarthritis. , 2011, Bioorganic & medicinal chemistry letters.

[12]  Ulf Norinder,et al.  Automated QSAR with a Hierarchy of Global and Local Models , 2011, Molecular informatics.

[13]  P. Hajduk,et al.  Cheminformatic tools for medicinal chemists. , 2010, Journal of medicinal chemistry.

[14]  R. A. Thompson,et al.  Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle. , 2012, Drug discovery today.

[15]  Yvonne Connolly Martin What Works and What Does Not: Lessons from Experience in a Pharmaceutical Company , 2007 .

[16]  Matthew T Stahl Open-source software: not quite endsville. , 2005, Drug discovery today.

[17]  Paul M. Selzer,et al.  Web-based cheminformatics tools deployed via corporate Intranets , 2004 .

[18]  John A. Lowe,et al.  A guide to drug discovery: The role of the medicinal chemist in drug discovery — then and now , 2004, Nature Reviews Drug Discovery.

[19]  Thierry Kogej,et al.  Making every SAR point count: the development of Chemistry Connect for the large-scale integration of structure and bioactivity data. , 2011, Drug discovery today.

[20]  Man-Ling Lee,et al.  DEGAS: Sharing and Tracking Target Compound Ideas with External Collaborators , 2012, J. Chem. Inf. Model..