Data-driven medicinal chemistry in the era of big data.

Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.

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

[2]  Daniel A. Gschwend,et al.  ArQiologist: An Integrated Decision Support Tool for Lead Optimization , 2005, J. Chem. Inf. Model..

[3]  Jan-Willem Boiten,et al.  Integrated Project Views: Decision Support Platform for Drug Discovery Project Teams , 2012, J. Chem. Inf. Model..

[4]  David S. Goodsell,et al.  The RCSB Protein Data Bank: new resources for research and education , 2012, Nucleic Acids Res..

[5]  W. Pardridge,et al.  Blood-brain barrier delivery. , 2007, Drug discovery today.

[6]  D. Judd,et al.  Open Innovation in Drug Discovery research comes of age. , 2013, Drug discovery today.

[7]  David R Cheshire.  How well do medicinal chemists learn from experience? , 2011, Drug discovery today.

[8]  A. Jordan,et al.  Drug Discovery Chemistry: a Primer for the Non-specialist , 2022 .

[9]  C. Lipinski.  Drug-like properties and the causes of poor solubility and poor permeability. , 2000, Journal of pharmacological and toxicological methods.

[10]  D. Swinney,et al.  How were new medicines discovered? , 2011, Nature Reviews Drug Discovery.

[11]  P. Popelier,et al.  In silico techniques for the identification of bioisosteric replacements for drug design. , 2010, Current topics in medicinal chemistry.

[12]  Wolfgang Guba,et al.  Development of a virtual screening method for identification of "frequent hitters" in compound libraries. , 2002, Journal of medicinal chemistry.

[13]  A. Hopkins,et al.  Ligand efficiency: a useful metric for lead selection. , 2004, Drug discovery today.

[14]  Matthew D Segall,et al.  Multi-parameter optimization: identifying high quality compounds with a balance of properties. , 2012, Current pharmaceutical design.

[15]  Patrick Marichal,et al.  Data reduction and representation in drug discovery. , 2007, Drug discovery today.

[16]  Stephen R Johnson,et al.  To measure is to know: an approach to CADD performance metrics. , 2011, Drug discovery today.

[17]  Michael S Lajiness,et al.  Assessment of the consistency of medicinal chemists in reviewing sets of compounds. , 2004, Journal of medicinal chemistry.

[18]  Gerhard Klebe,et al.  Adding calorimetric data to decision making in lead discovery: a hot tip , 2010, Nature Reviews Drug Discovery.

[19]  I. Muegge,et al.  Virtual screening for kinase targets. , 2004, Current medicinal chemistry.

[20]  Hilmar Weinmann Dr. and,et al.  Editorial: Drug Discovery Process for Kinease Inhibitors , 2005 .

[21]  D. Sprous,et al.  QSAR in the pharmaceutical research setting: QSAR models for broad, large problems. , 2010, Current topics in medicinal chemistry.

[22]  David Hampton,et al.  The hidden quality gap in discovery. , 2013, Drug discovery today.

[23]  Michael C. Hutter,et al.  Bioisosteric Similarity of Molecules Based on Structural Alignment and Observed Chemical Replacements in Drugs , 2009, J. Chem. Inf. Model..

[24]  J. Bajorath,et al.  BindingDB and ChEMBL: online compound databases for drug discovery , 2011, Expert opinion on drug discovery.

[25]  Jan Hoflack,et al.  REALISIS: A Medicinal Chemistry-Oriented Reagent Selection, Library Design, and Profiling Platform , 2004, J. Chem. Inf. Model..

[26]  Hilmar Weinmann,et al.  Editorial: Drug Discovery Process for Kinease Inhibitors , 2005 .

[27]  A. Ghose,et al.  Knowledge-based chemoinformatic approaches to drug discovery. , 2006, Drug discovery today.

[28]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[29]  Philip Smith,et al.  Lead optimization in 12 months? True confessions of a chemistry team. , 2001, Drug discovery today.

[31]  Konstantin V. Balakin,et al.  Property-Based Design of GPCR-Targeted Library , 2002, J. Chem. Inf. Comput. Sci..

[32]  J R Chretien,et al.  Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. , 1998, Journal of drug targeting.

[33]  Nathan Brown,et al.  Multi-objective optimization methods in drug design. , 2013, Drug discovery today. Technologies.

[34]  Ted Slater,et al.  Beyond data integration. , 2008, Drug discovery today.

[35]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[36]  Hans-Joachim Böhm,et al.  A guide to drug discovery: Hit and lead generation: beyond high-throughput screening , 2003, Nature Reviews Drug Discovery.

[37]  Chris Mattmann,et al.  Computing: A vision for data science , 2013, Nature.

[38]  J. Kazius,et al.  Derivation and validation of toxicophores for mutagenicity prediction. , 2005, Journal of medicinal chemistry.

[39]  A. Chadwick,et al.  Overcoming psychological barriers to good discovery decisions. , 2010, Drug discovery today.

[40]  Peter Ertl,et al.  The graphical representation of ADME-related molecule properties for medicinal chemists. , 2011, Drug discovery today.

[41]  Allan M Jordan,et al.  The medicinal chemist's toolbox: an analysis of reactions used in the pursuit of drug candidates. , 2011, Journal of medicinal chemistry.

[42]  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.

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

[44]  Nathan Brown,et al.  Molecular optimization using computational multi-objective methods. , 2007, Current opinion in drug discovery & development.

[45]  Nicolas Fay,et al.  The role of the informatics framework in early lead discovery. , 2006, Drug discovery today.

[46]  P. Leeson,et al.  The influence of drug-like concepts on decision-making in medicinal chemistry , 2007, Nature Reviews Drug Discovery.

[47]  Yanli Wang,et al.  PubChem: Integrated Platform of Small Molecules and Biological Activities , 2008 .

[48]  C. Johnstone.  Medicinal chemistry matters - a call for discipline in our discipline. , 2012, Drug discovery today.

[49]  Thierry Langer,et al.  The Protein Data Bank (PDB), its related services and software tools as key components for in silico guided drug discovery. , 2008, Journal of medicinal chemistry.

[50]  Ross McGuire,et al.  A molecular informatics view on best practice in multi-parameter compound optimization. , 2011, Drug discovery today.

[51]  Jürgen Bajorath,et al.  Mapping of pharmacological space , 2011, Expert opinion on drug discovery.

[52]  C. Lipinski.  Lead- and drug-like compounds: the rule-of-five revolution. , 2004, Drug discovery today. Technologies.

[53]  L. Knutsen.  Drug discovery management, small is still beautiful: Why a number of companies get it wrong. , 2011, Drug discovery today.

[54]  J. Medina-Franco,et al.  Shifting from the single to the multitarget paradigm in drug discovery. , 2013, Drug discovery today.

[55]  Dominic P. Williams,et al.  Toxicophores: groups and metabolic routes associated with increased safety risk. , 2002, Current opinion in drug discovery & development.

[56]  M. Murcko,et al.  Kinase-likeness and kinase-privileged fragments: toward virtual polypharmacology. , 2008, Journal of medicinal chemistry.

[57]  Chris Harbron,et al.  In search of preclinical robustness , 2012, Nature Reviews Drug Discovery.

[58]  M. Waring,et al.  A chemistry wiki to facilitate and enhance compound design in drug discovery. , 2013, Drug discovery today.

[59]  M. Hann,et al.  Finding the sweet spot: the role of nature and nurture in medicinal chemistry , 2012, Nature Reviews Drug Discovery.

[60]  S. Bryant,et al.  PubChem as a public resource for drug discovery. , 2010, Drug discovery today.

[61]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[62]  Meir Glick,et al.  Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery , 2012, PloS one.

[63]  Sam Motherwell.  Cheminformatics and Crystallography. The Cambridge Structural Database , 2004 .

[64]  Sean Ekins,et al.  Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. , 2012, Drug discovery today.

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

[66]  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..

[67]  Markus Wagener,et al.  The Quest for Bioisosteric Replacements , 2006, J. Chem. Inf. Model..

[68]  Patrick Schnider,et al.  ROCK: the Roche medicinal chemistry knowledge application - design, use and impact. , 2011, Drug discovery today.

[69]  Yanli Wang,et al.  PubChem: a public information system for analyzing bioactivities of small molecules , 2009, Nucleic Acids Res..

[70]  John P. Overington,et al.  Role of open chemical data in aiding drug discovery and design. , 2010, Future medicinal chemistry.

[71]  Tudor I. Oprea,et al.  Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces. , 2000, Journal of Molecular Graphics and Modelling.

[72]  Jonas Boström,et al.  Exploiting personalized information for reagent selection in drug design. , 2011, Drug discovery today.

[73]  J. Willem M. Nissink,et al.  Simple Size-Independent Measure of Ligand Efficiency , 2009, J. Chem. Inf. Model..

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

[75]  Roman Boutellier,et al.  Design principles for innovative workspaces to increase efficiency in pharmaceutical R&D: lessons learned from the Novartis campus. , 2013, Drug discovery today.

[76]  Nigel Greene,et al.  Physicochemical drug properties associated with in vivo toxicological outcomes: a review , 2009, Expert opinion on drug metabolism & toxicology.

[77]  L. Hothorn.  Biostatistics in nonclinical and preclinical drug development , 2005, Biometrical journal. Biometrische Zeitschrift.

[78]  Alexander Hillisch,et al.  Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. , 2006, Drug discovery today.

[79]  Thomas Sander,et al.  OSIRIS, an Entirely in-House Developed Drug Discovery Informatics System , 2009, J. Chem. Inf. Model..

[80]  Antony J Williams,et al.  Internet-based tools for communication and collaboration in chemistry. , 2008, Drug discovery today.

[81]  Sung Jin Cho,et al.  ADAAPT: Amgen's data access, analysis, and prediction tools , 2006, J. Comput. Aided Mol. Des..

[82]  Dennis Lendrem.  Statistical support to non‐clinical , 2002 .

[83]  D J Diller,et al.  The different strategies for designing GPCR and kinase targeted libraries. , 2004, Combinatorial chemistry & high throughput screening.

[84]  Bin Zhou,et al.  Large-Scale Annotation of Small-Molecule Libraries Using Public Databases , 2007, J. Chem. Inf. Model..

[85]  M. Gilson,et al.  Public domain databases for medicinal chemistry. , 2012, Journal of medicinal chemistry.

[86]  Ahlberg.  Visual exploration of HTS databases: bridging the gap between chemistry and biology. , 1999, Drug discovery today.

[87]  Max Kuhn,et al.  The use of the R language for medicinal chemistry applications. , 2012, Current topics in medicinal chemistry.

[88]  Sean Ekins,et al.  Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. , 2009, Drug discovery today.