Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?

AI comes to lead optimization: medicinal chemistry in all disease areas can be accelerated by exploiting our pre-competitive knowledge in an unbiased way.

[1]  M. Stahl,et al.  How Many Molecules Does It Take to Tell a Story? Case Studies, Language, and an Epistemic View of Medicinal Chemistry , 2015, ChemMedChem.

[2]  Jonas Boström,et al.  Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity , 2014, Journal of medicinal chemistry.

[3]  P. Darke,et al.  L-735,524: the design of a potent and orally bioavailable HIV protease inhibitor. , 1994, Journal of medicinal chemistry.

[4]  W. Hagmann,et al.  The many roles for fluorine in medicinal chemistry. , 2008, Journal of medicinal chemistry.

[5]  Paul J. Feltovich,et al.  The Cambridge handbook of expertise and expert performance , 2006 .

[6]  R. M. Francis,et al.  No Denying It: Medicinal Chemistry Training Is in Big Trouble. , 2016 .

[7]  Julian E. Fuchs,et al.  Matched molecular pair analysis: significance and the impact of experimental uncertainty. , 2014, Journal of medicinal chemistry.

[8]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[9]  Alexandre Varnek,et al.  Estimation of the size of drug-like chemical space based on GDB-17 data , 2013, Journal of Computer-Aided Molecular Design.

[10]  Gilles Klopman.  ARTIFICIAL INTELLIGENCE APPROACH TO STRUCTURE-ACTIVITY STUDIES. COMPUTER AUTOMATED STRUCTURE EVALUATION OF BIOLOGICAL ACTIVITY OF ORGANIC MOLECULES , 1985 .

[11]  Peter Norvig,et al.  The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.

[12]  B. Beno,et al.  A Survey of the Role of Noncovalent Sulfur Interactions in Drug Design. , 2015, Journal of medicinal chemistry.

[13]  Robert P. Sheridan,et al.  Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..

[14]  Jonas Boström,et al.  Analysis of Past and Present Synthetic Methodologies on Medicinal Chemistry: Where Have All the New Reactions Gone? , 2016, Journal of medicinal chemistry.

[15]  A. Vulpetti,et al.  The experimental uncertainty of heterogeneous public K(i) data. , 2012, Journal of medicinal chemistry.

[16]  Ramasamy Uthurusamy,et al.  Evolving data into mining solutions for insights , 2002, CACM.

[17]  C. Joppke.  RUSSELL SAGE FOUNDATION , 2003 .

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

[19]  Exploring QSAR. , 1995, Environmental science & technology.

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

[21]  Maria F. Sassano,et al.  Automated design of ligands to polypharmacological profiles , 2012, Nature.

[22]  Sanjay Joshua Swamidass,et al.  Sharing Chemical Relationships Does Not Reveal Structures , 2014, J. Chem. Inf. Model..

[23]  M. Borenstein,et al.  Publication Bias in Meta-Analysis , 2006 .

[24]  Jens Sadowski,et al.  Structure Modification in Chemical Databases , 2005 .

[25]  P. Hajduk,et al.  Statistical analysis of the effects of common chemical substituents on ligand potency. , 2008, Journal of medicinal chemistry.

[26]  Carol A Marchant,et al.  In Silico Tools for Sharing Data and Knowledge on Toxicity and Metabolism: Derek for Windows, Meteor, and Vitic , 2008, Toxicology mechanisms and methods.

[27]  J. Bajorath,et al.  Local structural changes, global data views: graphical substructure-activity relationship trailing. , 2011, Journal of medicinal chemistry.

[28]  M. Gleeson.  Generation of a set of simple, interpretable ADMET rules of thumb. , 2008, Journal of medicinal chemistry.

[29]  Edward A. Feigenbaum,et al.  Some challenges and grand challenges for computational intelligence , 2003, JACM.

[30]  S. Enoch,et al.  Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs , 2017, J. Chem. Inf. Model..

[31]  Günter Klambauer,et al.  DeepTox: Toxicity Prediction using Deep Learning , 2016, Front. Environ. Sci..

[32]  Scott Boyer,et al.  A critical assessment of modeling safety-related drug attrition , 2013 .

[33]  Christian Tyrchan,et al.  Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations , 2016, Computational and structural biotechnology journal.

[34]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

[35]  T. Fujita,et al.  Background and features of emil, a system for database-aided bioanalogous structural transformation of bioactive compounds , 1995 .

[36]  Tudor I. Oprea,et al.  A comprehensive map of molecular drug targets , 2016, Nature Reviews Drug Discovery.

[37]  J. Wells,et al.  Small-molecule inhibitors of protein-protein interactions: progressing toward the reality. , 2014, Chemistry & biology.

[38]  Jérôme Hert,et al.  Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA). , 2017, Journal of medicinal chemistry.

[39]  Johann Gasteiger,et al.  A new treatment of chemical reactivity: Development of EROS, an expert system for reaction prediction and synthesis design , 1987 .

[40]  K. Stewart,et al.  Drug Guru: a computer software program for drug design using medicinal chemistry rules. , 2006, Bioorganic & medicinal chemistry.

[41]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[42]  J. Dearden,et al.  How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR) , 2009, SAR and QSAR in environmental research.

[43]  E J Corey,et al.  Computer-assisted design of complex organic syntheses. , 1969, Science.

[44]  Simon J F Macdonald,et al.  Passing on the medicinal chemistry baton: training undergraduates to be industry-ready through research projects between the University of Nottingham and GlaxoSmithKline. , 2016, Drug discovery today.

[45]  J. Scannell,et al.  Diagnosing the decline in pharmaceutical R&D efficiency , 2012, Nature Reviews Drug Discovery.

[46]  Carl Petersson,et al.  In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development. , 2017, Journal of medicinal chemistry.

[47]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[48]  J. C. Emmett,et al.  Characterization and development of cimetidine as a histamine H2-receptor antagonist , 1978 .

[49]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[50]  Andrew G. Leach,et al.  Matched molecular pair analysis in drug discovery. , 2013, Drug discovery today.

[51]  Bernd Beck,et al.  Fuzzy Matched Pairs: A Means To Determine the Pharmacophore Impact on Molecular Interaction , 2014, J. Chem. Inf. Model..

[52]  W Patrick Walters,et al.  What do medicinal chemists actually make? A 50-year retrospective. , 2011, Journal of medicinal chemistry.

[53]  Davide Castelvecchi,et al.  Can we open the black box of AI? , 2016, Nature.

[54]  Andrew G. Leach,et al.  Rationally designing safer anilines: the challenging case of 4-aminobiphenyls. , 2012, Journal of medicinal chemistry.

[55]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[56]  P. Timmermans,et al.  Rationale for the chemical development of angiotensin II receptor antagonists. , 1992, American journal of hypertension.

[57]  T. Greenhalgh,et al.  Education and debate , 1997 .

[58]  David J. Nicholls,et al.  Impact of a five-dimensional framework on R&D productivity at AstraZeneca , 2018, Nature Reviews Drug Discovery.

[59]  N. Meanwell.  Synopsis of some recent tactical application of bioisosteres in drug design. , 2011, Journal of medicinal chemistry.

[60]  C. Humblet,et al.  Escape from flatland: increasing saturation as an approach to improving clinical success. , 2009, Journal of medicinal chemistry.

[61]  Gisbert Schneider,et al.  De Novo Design of Bioactive Small Molecules by Artificial Intelligence , 2018, Molecular informatics.

[62]  Christopher E Keefer,et al.  The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction. , 2017, MedChemComm.

[63]  Peter W. Kenny,et al.  Inflation of correlation in the pursuit of drug-likeness , 2013, Journal of Computer-Aided Molecular Design.

[64]  G. Klopman.  Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules , 1985 .

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

[66]  Gisbert Schneider,et al.  Automating drug discovery , 2017, Nature Reviews Drug Discovery.

[67]  Dave Allen.  Where will we get the next generation of medicinal chemists? , 2016, Drug discovery today.

[68]  Ernesto Callegari,et al.  A comprehensive listing of bioactivation pathways of organic functional groups. , 2005, Current drug metabolism.

[69]  Jack W Scannell,et al.  When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis , 2016, PloS one.

[70]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[71]  M. D. Hill,et al.  Applications of Fluorine in Medicinal Chemistry. , 2015, Journal of medicinal chemistry.

[72]  Joshua Lederberg,et al.  DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation , 1993, Artif. Intell..

[73]  M. Congreve,et al.  A 'rule of three' for fragment-based lead discovery? , 2003, Drug discovery today.

[74]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[75]  Sepp Hochreiter,et al.  Toxicity Prediction using Deep Learning , 2015, ArXiv.

[76]  Visakan Kadirkamanathan,et al.  Lead Optimization Using Matched Molecular Pairs: Inclusion of Contextual Information for Enhanced Prediction of hERG Inhibition, Solubility, and Lipophilicity , 2010, J. Chem. Inf. Model..

[77]  Jameed Hussain,et al.  Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets , 2010, J. Chem. Inf. Model..

[78]  A. Whitty,et al.  Progress towards the broad use of non-peptide synthetic macrocycles in drug discovery. , 2017, Organic and biomolecular chemistry.

[79]  Thierry Kogej,et al.  Big pharma screening collections: more of the same or unique libraries? The AstraZeneca-Bayer Pharma AG case. , 2013, Drug discovery today.

[80]  Andrew G. Leach,et al.  Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. , 2006, Journal of medicinal chemistry.

[81]  Nathan Brown,et al.  MOARF, an Integrated Workflow for Multiobjective Optimization: Implementation, Synthesis, and Biological Evaluation , 2015, J. Chem. Inf. Model..