, Chemicals Abstracts Service

W. A. Warr, A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility, Mol Inform, vol.33, issue.6-7, pp.469-476, 2014.

S. Szymkuc, E. P. Gajewska, T. Klucznik, K. Molga, P. Dittwald et al., Computer-assisted synthetic planning: the end of the beginning, Angew Chem Int Ed Engl, vol.55, issue.20, pp.5904-5937, 2016.

G. Schneider, Future de novo drug design, Mol Inform, vol.33, issue.6-7, pp.397-402, 2014.

Y. Moriya, D. Shigemizu, M. Hattori, T. Tokimatsu, M. Kotera et al., PathPred: an enzyme-catalyzed metabolic pathway prediction server, Nucleic Acids Res, vol.38, pp.138-143, 2010.

J. G. Jeffryes, R. L. Colastani, M. Elbadawi-sidhu, T. Kind, T. D. Niehaus et al., MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics, J Cheminform, vol.7, p.44, 2015.

P. Carbonell, P. Parutto, J. Herisson, S. B. Pandit, and J. L. Faulon, XTMS: pathway design in an eXTended metabolic space, Nucleic Acids Res, vol.42, pp.389-394, 2014.
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N. Hadadi, J. Hafner, A. Shajkofci, A. Zisaki, and V. Hatzimanikatis, ATLAS of biochemistry: a repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies, ACS Synth Biol, vol.5, issue.10, pp.1155-1166, 2016.

H. Yim, R. Haselbeck, W. Niu, C. Pujol-baxley, A. Burgard et al., Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol, Nat Chem Biol, vol.7, issue.7, pp.445-452, 2011.

M. Liu, B. Bienfait, O. Sacher, J. Gasteiger, R. J. Siezen et al., Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering, PLoS ONE, vol.9, issue.1, p.84769, 2014.

S. Moretti, O. Martin, T. Van-du-tran, A. Bridge, A. Morgat et al., MetaNetX/MNXref-reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks, Nucleic Acids Res, vol.44, issue.D1, pp.523-526, 2016.

I. Ugi, J. Bauer, K. Bley, A. Dengler, A. Dietz et al., Computer-assisted solution of chemical problems-the historical development and the present state of the art of a new discipline of chemistry, Angew Chem Int Ed Engl, vol.32, issue.2, pp.164-189, 1993.

P. Carbonell, A. G. Planson, D. Fichera, and J. L. Faulon, A retrosynthetic biology approach to metabolic pathway design for therapeutic production, BMC Syst Biol, vol.5, p.122, 2011.

N. Hadadi and V. Hatzimanikatis, Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways, Curr Opin Chem Biol, vol.28, pp.99-104, 2015.

J. Faulon and P. Carbonell, Reaction network generation, Handbook of chemoinformatics algorithms, 2010.

B. Delépine, T. Duigou, P. Carbonell, and J. L. Faulon, RetroPath2.0: A retrosynthesis workflow for metabolic engineers, Metab Eng, 2017.

M. R. Berthold, (eds) Data analysis, machine learning and applications. Studies in classification, data analysis, and knowledge organization, pp.319-326, 2008.

G. Landrum, RDKit: open-source cheminformatics, vol.2, 2016.

T. Rodrigues, D. Reker, M. Welin, M. Caldera, C. Brunner et al., De novo fragment design for drug discovery and chemical biology, Angew Chem Int Ed Engl, vol.54, issue.50, pp.15079-15083, 2015.

J. Mellor, I. Grigoras, P. Carbonell, and J. L. Faulon, Semisupervised Gaussian process for automated enzyme search, ACS Synth Biol, vol.5, issue.6, pp.518-528, 2016.

T. Feher, A. G. Planson, P. Carbonell, A. Fernandez-castane, I. Grigoras et al., Validation of RetroPath, a computeraided design tool for metabolic pathway engineering, Biotechnol J, vol.9, issue.11, pp.1446-1457, 2014.

H. L. Rost, U. Schmitt, R. Aebersold, and L. Malmstrom, pyOpenMS: a python-based interface to the OpenMS mass-spectrometry algorithm library, Proteomics, vol.14, issue.1, pp.74-77, 2014.

D. Thiagarajan and D. P. Mehta, Faster algorithms for isomer network generation, J Chem Inf Model, vol.56, issue.12, pp.2310-2319, 2016.

J. E. Peironcely, M. Rojas-cherto, D. Fichera, T. Reijmers, L. Coulier et al., OMG: open molecule generator, J Cheminform, vol.4, issue.1, p.21, 2012.

M. M. Jaghoori, S. Jongmans, F. De-boer, J. Peironcely, J. L. Faulon et al., PMG: multi-core metabolite identification. Electron, Notes Theor Comput Sci, vol.299, pp.53-60, 2013.
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J. M. Gally, S. Bourg, Q. Do, S. Aci-seche, and P. Bonnet, VSPrep: a general KNIME workflow for the Preparation of molecules for virtual screening, Mol Inf, vol.36, pp.1-11, 2017.

L. Ruddigkeit, R. Van-deursen, L. C. Blum, and J. L. Reymond, Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17, J Chem Inf Model, vol.52, issue.11, pp.2864-2875, 2012.

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P. Willett, Similarity-based virtual screening using 2D fingerprints, Drug Discov Today, vol.11, pp.1046-1053, 2006.

J. L. Durant, B. A. Leland, D. R. Henry, and J. G. Nourse, Reoptimization of MDL keys for use in drug discovery, J Chem Inf Comput Sci, vol.42, issue.6, pp.1273-1280, 2002.

W. M. Brown, S. Martin, M. D. Rintoul, and J. L. Faulon, Designing novel polymers with targeted properties using the signature molecular descriptor, J Chem Inf Model, vol.46, issue.2, pp.826-835, 2006.

M. N. Subramanian, Polymer properties, Polymer blends and composites: chemistry and technology, 2017.

J. C. Gerdeen and R. A. Rorrer, Engineering design with polymers and composites, vol.30, 2011.

C. J. Churchwell, M. D. Rintoul, S. Martin, D. P. Visco, A. Kotu et al., The signature molecular descriptor. 3. inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides, J Mol Graph Model, vol.22, issue.4, pp.263-273, 2004.

S. Martin, Lattice enumeration for inverse molecular design using the signature descriptor, J Chem Inf Model, vol.52, issue.7, pp.1787-1797, 2012.

P. Setny and J. Trylska, Search for novel aminoglycosides by combining fragment-based virtual screening and 3D-QSAR scoring, J Chem Inf Model, vol.49, issue.2, pp.390-400, 2009.

K. Kawai, N. Nagata, and Y. Takahashi, De novo design of drug-like molecules by a fragment-based molecular evolutionary approach, J Chem Inf Model, vol.54, issue.1, pp.49-56, 2014.

D. S. Wishart, T. Jewison, A. C. Guo, M. Wilson, C. Knox et al., HMDB 3.0-the human metabolome database in 2013, Nucleic Acids Res, vol.41, pp.801-807, 2013.

C. S. Henry, L. J. Broadbelt, and V. Hatzimanikatis, Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate, Biotechnol Bioeng, vol.106, issue.3, pp.462-473, 2010.

J. D. Orth, T. M. Conrad, J. Na, J. A. Lerman, H. Nam et al., A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011, Mol Syst Biol, vol.7, p.535, 2011.

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P. Kiefer, U. Schmitt, J. E. Muller, J. Hartl, F. Meyer et al., DynaMet: a fully automated pipeline for dynamic LC-MS data, Anal Chem, vol.87, pp.9679-9686, 2015.

S. A. Rahman, G. Torrance, L. Baldacci, S. Martinez-cuesta, F. Fenninger et al., Reaction Decoder Tool (RDT): extracting features from chemical reactions, Bioinformatics, vol.32, issue.13, pp.2065-2066, 2016.

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M. Walzer, T. Sachsenberg, F. Aicheler, M. Rurik, J. Veit et al., , 2017.

D. P. Visco, R. S. Pophale, M. D. Rintoul, and J. L. Faulon, Developing a methodology for an inverse quantitative structure-activity relationship using the signature molecular descriptor, J Mol Graph Model, vol.20, issue.6, pp.429-438, 2002.

R. Van-deursen and J. L. Reymond, Chemical space travel, ChemMed-Chem, vol.2, issue.5, pp.636-640, 2007.

M. J. Yu, Natural product-like virtual libraries: recursive atom-based enumeration, J Chem Inf Model, vol.51, issue.3, pp.541-557, 2011.

D. Hoksza, P. Skoda, M. Vorsilak, and D. Svozil, Molpher: a software framework for systematic chemical space exploration, J Cheminform, vol.6, issue.1, p.7, 2014.

A. M. Virshup, J. Contreras-garcia, P. Wipf, W. Yang, and D. N. Beratan, Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds, J Am Chem Soc, vol.135, pp.7296-7303, 2013.