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Volumn 7, Issue 1, 2017, Pages

Predicting the outcomes of organic reactions via machine learning: Are current descriptors sufficient?

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL BINDING; CHEMISTRY; MACHINE LEARNING;

EID: 85020943110     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-02303-0     Document Type: Article
Times cited : (97)

References (40)
  • 1
    • 84878979335 scopus 로고    scopus 로고
    • The big challenges of big data
    • Marx, V. The big challenges of big data. Nature 498, 255-260, doi:10.1038/498255a (2013).
    • (2013) Nature , vol.498 , pp. 255-260
    • Marx, V.1
  • 2
    • 51349115095 scopus 로고    scopus 로고
    • Big data: The future of biocuration
    • Howe D., et al. Big data: The future of biocuration. Nature 2008, 455, 47-50 (2008).
    • (2008) Nature 2008 , vol.455 , pp. 47-50
    • Howe, D.1
  • 3
    • 84875479835 scopus 로고    scopus 로고
    • Big (chemistry) data
    • Gibb, B. C. Big (chemistry) data. Nat. Chem. 5, 248-249, doi:10.1038/nchem.1604 (2013).
    • (2013) Nat. Chem. , vol.5 , pp. 248-249
    • Gibb, B.C.1
  • 4
    • 84892142922 scopus 로고    scopus 로고
    • The learning machines
    • Jones, N. The learning machines. Nature 505, 146-148, doi:10.1038/505146a (2014).
    • (2014) Nature , vol.505 , pp. 146-148
    • Jones, N.1
  • 5
    • 84937801713 scopus 로고    scopus 로고
    • Machine learning: Trends, perspectives, prospects
    • Jordan, M. I., Mitchell, T. M. Machine learning: Trends, perspectives, prospects. Science 349, 255-260, doi:10.1126/science. aaa8415 (2015).
    • (2015) Science , vol.349 , pp. 255-260
    • Jordan, M.I.1    Mitchell, T.M.2
  • 6
    • 85032751458 scopus 로고    scopus 로고
    • Deep neural networks for acoustic modeling in speech recognition.
    • Hilton, G., et al. Deep neural networks for acoustic modeling in speech recognition. IEEE Sign. Process. Mag. 29, 82-97, doi:10.1109/MSP.2012.2205597 (2012).
    • (2012) IEEE Sign. Process. Mag. , vol.29 , pp. 82-97
    • Hilton, G.1
  • 7
    • 80053074478 scopus 로고    scopus 로고
    • Image processing and machine learning for fully automated probabilistic evaluation of medical images
    • Sajn, L., Kukar, M. Image processing and machine learning for fully automated probabilistic evaluation of medical images. Comp. Meth. Prog. Biomed. 104, E75-E86, doi:10.1016/j.cmpb.2010.06.021 (2011).
    • (2011) Comp. Meth. Prog. Biomed. , vol.104 , pp. E75-E86
    • Sajn, L.1    Kukar, M.2
  • 8
    • 33644959172 scopus 로고    scopus 로고
    • Metabolomics modelling and machine learning in systems biology-towards an understanding of the languages of cells
    • Kell, D. B. Metabolomics, modelling and machine learning in systems biology-towards an understanding of the languages of cells. FEBS J. 273, 873-894, doi:10.1111/j.1742-4658.2006.05136.x (2006).
    • (2006) FEBS J. , vol.273 , pp. 873-894
    • Kell, D.B.1
  • 9
    • 84937786173 scopus 로고    scopus 로고
    • Economic reasoning and artificial intelligence
    • Parkes, D. C., Wellman, M. P. Economic reasoning and artificial intelligence. Science 349, 267-272, doi:10.1126/science.aaa8403 (2015).
    • (2015) Science , vol.349 , pp. 267-272
    • Parkes, D.C.1    Wellman, M.P.2
  • 10
    • 84862647729 scopus 로고    scopus 로고
    • Predicting a small molecule-kinase interaction map: A machine learning approach
    • Buchwald, F., Richter, L., Kramer, S. Predicting a small molecule-kinase interaction map: A machine learning approach. J. Cheminf. 3, 22 (2011).
    • (2011) J. Cheminf. , vol.3 , pp. 22
    • Buchwald, F.1    Richter, L.2    Kramer, S.3
  • 11
    • 77952768125 scopus 로고    scopus 로고
    • Ranking chemical structures for drug discovery: A new machine learning approach
    • Agarwal, S., Dugar, D., Sengupta, S. Ranking chemical structures for drug discovery: A new machine learning approach. J. Chem. Inf. Model. 50, 716-731, doi:10.1021/ci9003865 (2010).
    • (2010) J. Chem. Inf. Model. , vol.50 , pp. 716-731
    • Agarwal, S.1    Dugar, D.2    Sengupta, S.3
  • 12
    • 51349131079 scopus 로고    scopus 로고
    • Machine learning for in silico virtual screening and chemical genomics: New strategies
    • Vert, J.-P., Jacob, L. Machine learning for in silico virtual screening and chemical genomics: New strategies. Comb. Chem. High Throughput Screening 11, 677-685, doi:10.2174/138620708785739899 (2008).
    • (2008) Comb. Chem. High Throughput Screening , vol.11 , pp. 677-685
    • Vert, J.-P.1    Jacob, L.2
  • 13
    • 84880542260 scopus 로고    scopus 로고
    • Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules
    • Lusci, A., Pollastri, G., Baldi, P. Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules. J. Chem. Inf. Model. 53, 1563-1575, doi:10.1021/ci400187y (2013).
    • (2013) J. Chem. Inf. Model. , vol.53 , pp. 1563-1575
    • Lusci, A.1    Pollastri, G.2    Baldi, P.3
  • 14
    • 17644370009 scopus 로고    scopus 로고
    • Ab initio modelling-Genesis of crystal structures
    • van de Walle, A. Ab initio modelling-Genesis of crystal structures. Nat. Mater. 4, 362-363, doi:10.1038/nmat1378 (2005).
    • (2005) Nat. Mater. , vol.4 , pp. 362-363
    • Van De Walle, A.1
  • 15
    • 84946476177 scopus 로고    scopus 로고
    • Learning from the harvard clean energy project: The use of neural networks to accelerate materials discovery
    • Pyzer-Knapp, E. O., Li, K., Aspuru-Guzik, A. Learning from the Harvard Clean Energy Project: The use of neural networks to accelerate materials discovery. Adv. Funct. Mater. 25, 6495-6502, doi:10.1038/sdata.2016.86 (2015).
    • (2015) Adv. Funct. Mater. , vol.25 , pp. 6495-6502
    • Pyzer-Knapp, E.O.1    Li, K.2    Aspuru-Guzik, A.3
  • 16
    • 84969849662 scopus 로고    scopus 로고
    • Machine-learning-assisted materials discovery using failed experiments
    • Raccuglia, R., et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73-76, doi:10.1038/nature17439 (2016).
    • (2016) Nature , vol.533 , pp. 73-76
    • Raccuglia, R.1
  • 17
    • 84921799235 scopus 로고    scopus 로고
    • Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity
    • Schneider, N., Lowe, D. M., Sayle, R. A., Landrum, G. A. Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity. J. Chem. Inf. Model. 55, 39-53, doi:10.1021/ci5006614 (2015).
    • (2015) J. Chem. Inf. Model. , vol.55 , pp. 39-53
    • Schneider, N.1    Lowe, D.M.2    Sayle, R.A.3    Landrum, G.A.4
  • 18
    • 77953313401 scopus 로고    scopus 로고
    • Application of molecular topology for the prediction of the reaction times and yields under solvent-free conditions
    • Gálvez, J., Gálvez-Llompart, M., Garciá-Domenech, R. Application of molecular topology for the prediction of the reaction times and yields under solvent-free conditions. Green Chem. 12, 1056-1061, doi:10.1039/b926047a (2010).
    • (2010) Green Chem. , vol.12 , pp. 1056-1061
    • Gálvez, J.1    Gálvez-Llompart, M.2    Garciá-Domenech, R.3
  • 19
    • 79952264916 scopus 로고    scopus 로고
    • Application of molecular topology for the prediction of reaction yields and anti-Inflammatory activity of heterocyclic amidine derivatives
    • Pla-Franco, J., Gálvez-Llompart, M., Gálvez, J., Garciá-Domenech, R. Application of molecular topology for the prediction of reaction yields and anti-Inflammatory activity of heterocyclic amidine derivatives. Int. J. Mol. Sci. 12, 1281-1292, doi:10.3390/ijms12021281 (2011).
    • (2011) Int. J. Mol. Sci. , vol.12 , pp. 1281-1292
    • Pla-Franco, J.1    Gálvez-Llompart, M.2    Gálvez, J.3    Garciá-Domenech, R.4
  • 20
    • 84867780136 scopus 로고    scopus 로고
    • Reaction Predictor: Prediction of complex chemical reactions at the mechanistic level using machine learning
    • Kayala, M. A., Baldi, P. ReactionPredictor: Prediction of complex chemical reactions at the mechanistic level using machine learning. J. Chem. Inf. Model. 52, 2526-2540, doi:10.1021/ci3003039 (2012).
    • (2012) J. Chem. Inf. Model. , vol.52 , pp. 2526-2540
    • Kayala, M.A.1    Baldi, P.2
  • 21
    • 85012967324 scopus 로고    scopus 로고
    • Neural networks for the prediction organic chemistry reactions
    • Wei, J. N., Duvenaud, D., Aspuru-Guzik, A. Neural networks for the prediction organic chemistry reactions. ACS Central Science 2, (725-732 (2016).
    • (2016) ACS Central Science , vol.2 , pp. 725-732
    • Wei, J.N.1    Duvenaud, D.2    Aspuru-Guzik, A.3
  • 22
    • 84940719783 scopus 로고    scopus 로고
    • A priori estimation of organic reaction yields
    • Emami, F. S., et al. A priori estimation of organic reaction yields. Angew. Chem. Int. Ed. 54, 10797-10801, doi:10.1002/anie.201503890 (2015).
    • (2015) Angew. Chem. Int. Ed. , vol.54 , pp. 10797-10801
    • Emami, F.S.1
  • 23
    • 84981765197 scopus 로고    scopus 로고
    • Computer-assisted synthetic planning: The end of the beginning
    • Szymku, S., et al. Computer-assisted synthetic planning: The end of the beginning. Angew. Chem. Int. Ed. 55, 5904-5937, doi:10.1002/anie.201506101 (2016).
    • (2016) Angew. Chem. Int. Ed. , vol.55 , pp. 5904-5937
    • Szymku, S.1
  • 24
    • 85050579669 scopus 로고    scopus 로고
    • New directions for machine learning
    • Wilson, E. K. New directions for machine learning. Chem., Eng. News 4, 29-30 (2017).
    • (2017) Chem., Eng. News , vol.4 , pp. 29-30
    • Wilson, E.K.1
  • 26
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes, C., Vapnik, V. Support-vector networks. Mach. Learn. 20, 273-297, doi:10.1007/BF00994018 (1995).
    • (1995) Mach. Learn. , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 28
    • 33748242731 scopus 로고
    • Neural networks in chemistry
    • Gasteiger, J., Zupan, J. Neural networks in chemistry. Angew. Chem. Int. Ed. 32, 503-527, doi:10.1002/(ISSN)1521-3773 (1993).
    • (1993) Angew. Chem. Int. Ed. , vol.32 , pp. 503-527
    • Gasteiger, J.1    Zupan, J.2
  • 29
    • 33646430006 scopus 로고    scopus 로고
    • Extremely randomized trees
    • Geurts, P., Ernst, D., Wehenkel, L. Extremely randomized trees. Mach. Learn. 63, 3-42, doi:10.1007/s10994-006-6226-1 (2006).
    • (2006) Mach. Learn. , vol.63 , pp. 3-42
    • Geurts, P.1    Ernst, D.2    Wehenkel, L.3
  • 30
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. Random forests. Mach. Learn. 45, 5-32, doi:10.1023/A:1010933404324 (2001).
    • (2001) Mach. Learn. , vol.45 , pp. 5-32
    • Breiman, L.1
  • 33
    • 33845379303 scopus 로고
    • Atom pairs as molecular features in structure-activity studies: Definition and applications
    • Carhart, R. E., Smith, D. H., Venkataraghavan, R. J. Atom pairs as molecular features in structure-activity studies: Definition and applications. Chem. Inf. Model. 25, 64-73, doi:10.1021/ci00046a002 (1985).
    • (1985) Chem. Inf. Model. , vol.25 , pp. 64-73
    • Carhart, R.E.1    Smith, D.H.2    Venkataraghavan, R.J.3
  • 34
    • 84905364269 scopus 로고    scopus 로고
    • Organic chemistry as a language and the implications of chemical linguistics for structural and retrosynthetic analyses
    • Cadeddu, A., Wylie, E. K., Jurczak, J., Wampler-Doty, M., Grzybowski, B. A. Organic chemistry as a language and the implications of chemical linguistics for structural and retrosynthetic analyses. Angew. Chem. Int. Ed. 53, 8108-8112, doi:10.1002/anie.201403708 (2014).
    • (2014) Angew. Chem. Int. Ed. , vol.53 , pp. 8108-8112
    • Cadeddu, A.1    Wylie, E.K.2    Jurczak, J.3    Wampler-Doty, M.4    Grzybowski, B.A.5
  • 35
    • 77950537175 scopus 로고    scopus 로고
    • Regularization paths for generalized linear models via coordinate descent
    • Friedman, J., Hastie, T., Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Soft. 33, 1-22, doi:10.18637/jss.v033.i01 (2010).
    • (2010) J. Stat. Soft. , vol.33 , pp. 1-22
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 36
    • 68949140728 scopus 로고    scopus 로고
    • A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
    • Menze, B. H., et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10, 213, doi:10.1186/1471-2105-10-213 (2009).
    • (2009) BMC Bioinformatics , vol.10 , pp. 213
    • Menze, B.H.1
  • 37
    • 0028938112 scopus 로고
    • Total synthesis of (+/)-FR-900482
    • Schkeryantz, J. M., Danishefsky, S. J. Total synthesis of (+/)-FR-900482. J. Am. Chem. Soc. 117, 4722-4723, doi:10.1021/ja00121a037 (1995).
    • (1995) J. Am. Chem. Soc. , vol.117 , pp. 4722-4723
    • Schkeryantz, J.M.1    Danishefsky, S.J.2
  • 38
    • 0038614445 scopus 로고    scopus 로고
    • Novel tunable CuX2-mediated cyclization reaction of cyclopropylideneacetic acids and esters for the facile synthesis of 4-halomethyl-2(5H)-furanones and 4-halo-5, 6-dihydro-2H-pyran-2-ones
    • Huang, X., Zhou, H. W. Novel tunable CuX2-mediated cyclization reaction of cyclopropylideneacetic acids and esters for the facile synthesis of 4-halomethyl-2(5H)-furanones and 4-halo-5, 6-dihydro-2H-pyran-2-ones. Org. Lett. 4, 4419-4422, doi:10.1021/ol026911q (2002).
    • (2002) Org. Lett. , vol.4 , pp. 4419-4422
    • Huang, X.1    Zhou, H.W.2
  • 39
    • 0001626504 scopus 로고
    • Charge as a key component in reaction design-The invention of cationic cyclization reactions of importance in synthesis
    • Overman, L. E. Charge as a key component in reaction design-the invention of cationic cyclization reactions of importance in synthesis. Acc. Chem. Res. 25, 352-359, doi:10.1021/ar00020a005 (1992).
    • (1992) Acc. Chem. Res. , vol.25 , pp. 352-359
    • Overman, L.E.1
  • 40
    • 0030001543 scopus 로고    scopus 로고
    • The total synthesis of dynemicin A leading to development of a fully contained bioreductively activated enediyne prodrug
    • Shair, M. D., Yoon, T. Y., Mosny, K. K., Chou, T. C., Danishefsky, S. J. The total synthesis of dynemicin A leading to development of a fully contained bioreductively activated enediyne prodrug. J. Am. Chem. Soc. 118, 9509-9525, doi:10.1021/ja960040w (1996).
    • (1996) J. Am. Chem. Soc. , vol.118 , pp. 9509-9525
    • Shair, M.D.1    Yoon, T.Y.2    Mosny, K.K.3    Chou, T.C.4    Danishefsky, S.J.5


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