Computational prediction of chemical reactions: current status and outlook

Drug Discovery Today - Tập 23 - Trang 1203-1218 - 2018
Ola Engkvist1, Per-Ola Norrby2, Nidhal Selmi1, Yu-hong Lam3, Zhengwei Peng3, Edward C. Sherer3, Willi Amberg4, Thomas Erhard4, Lynette A. Smyth4
1Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, SE-43183 Mölndal, Sweden
2Pharmaceutical Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, SE-43183 Mölndal, Sweden
3Modeling and Informatics, MRL, Merck & Co., Rahway, NJ 07065, USA
4AbbVie Deutschland GmbH & Co. KG, Neuroscience Discovery, Medicinal Chemistry, Knollstrasse, 67061 Ludwigshafen, Germany

Tài liệu tham khảo

Warr, 2014, A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility, Mol. Inf., 33, 469, 10.1002/minf.201400052 Tomkinson, 2014 Agnetti, 2013 Schneider, 2015, Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity, J. Chem. Inf. Model., 55, 39, 10.1021/ci5006614 Schneider, 2016, Big data from pharmaceutical patents: a computational analysis of medicinal chemists’ bread and butter, J. Med. Chem., 59, 4385, 10.1021/acs.jmedchem.6b00153 Rahman, 2016, Reaction Decoder Tool (RDT): extracting features from chemical reactions, Bioinformatics, 32, 2065, 10.1093/bioinformatics/btw096 Hartenfeller, 2011, A collection of robust organic synthesis reactions for in silico molecule design, J. Chem. Inf. Model., 51, 3093, 10.1021/ci200379p Christ, 2012, Mining electronic laboratory notebooks: analysis, retrosynthesis, and reaction based enumeration, J. Chem. Inf. Model., 52, 1745, 10.1021/ci300116p Gelernter, 1990, Building and refining a knowledge base for synthetic organic-chemistry via the methodology of inductive and deductive machine learning, J. Chem. Inf. Comput. Sci., 30, 492, 10.1021/ci00068a023 Matosin, 2014, Negativity towards negative results: a discussion of the disconnect between scientific worth and scientific culture, Dis. Model. Mech., 7, 171, 10.1242/dmm.015123 Cooper, 2010, Factors determining the selection of organic reactions by medicinal chemists and the use of these reactions in arrays (small focused libraries), Angew. Chem. Int. Ed. Engl., 49, 8082, 10.1002/anie.201002238 Santanilla, 2015, Nanomole-scale high-throughput chemistry for the synthesis of complex molecules, Science, 347, 49, 10.1126/science.1259203 Tetko, 2016, Does ‘Big Data’ exist in medicinal chemistry, and if so, how can it be harnessed?, Future Med. Chem., 8, 1801, 10.4155/fmc-2016-0163 Grethe, 2013, International chemical identifier for reactions (RInChI), J. Cheminf., 5, 45, 10.1186/1758-2946-5-45 Grzybowski, 2009, The ‘wired’ universe of organic chemistry, Nat. Chem., 1, 31, 10.1038/nchem.136 Kayala, 2011, Learning to predict chemical reactions, J. Chem. Inf. Model., 51, 2209, 10.1021/ci200207y Kayala, 2012, ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning, J. Chem. Inf. Model., 52, 2526, 10.1021/ci3003039 Sadowski, 2016, Synergies between quantum mechanics and machine learning in reaction prediction, J. Chem. Inf. Model., 56, 2125, 10.1021/acs.jcim.6b00351 Carrera, 2009, Machine learning of chemical reactivity from databases of organic reactions, J. Comput.-Aided Mol. Des., 23, 419, 10.1007/s10822-009-9275-2 Zhang, 2005, Structure-based classification of chemical reactions without assignment of reaction centers, J. Chem. Inf. Model., 45, 1775, 10.1021/ci0502707 Silver, 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961 Coley, 2017, Prediction of organic reaction outcomes using machine learning, ACS Cent. Sci., 3, 434, 10.1021/acscentsci.7b00064 Wei, 2016, Neural networks for the prediction of organic chemistry reactions, ACS Cent. Sci., 2, 725, 10.1021/acscentsci.6b00219 Segler, 2017, Neural-symbolic machine learning for retrosynthesis and reaction prediction, Chemistry, 23, 5966, 10.1002/chem.201605499 Marcou, 2015, Expert system for predicting reaction conditions: the Michael reaction case, J. Chem. Inf. Model., 55, 239, 10.1021/ci500698a Lin, 2016, Automatized assessment of protective group reactivity: a step toward big reaction data analysis, J. Chem. Inf. Model., 56, 2140, 10.1021/acs.jcim.6b00319 Segler, 2017, Modelling chemical reasoning to predict and invent reactions, Chemistry, 23, 6118, 10.1002/chem.201604556 Segler, M. et al. Learning to plan chemical synthesis. https://arxiv.org/pdf/1708.04202.pdf. Accessed 9 March 2018. Liu, 2017, Retrosynthetic reaction prediction using neural sequence-to-sequence models, ACS Cent. Sci., 3, 1103, 10.1021/acscentsci.7b00303 www.pistoiaalliance.org/projects/udm/. [Accessed 26 February 2018] www.cas.org/etrain/scifinder/sciplanner.html. [Accessed 26 February 2018] https://service.elsevier.com/app/answers/detail/a_id/14597/supporthub/reaxys/. [Accessed 26 February 2018] Corey, 1969, Computer-assisted design of complex organic syntheses, Science, 166, 178, 10.1126/science.166.3902.178 Corey, 1985, Computer-assisted analysis in organic synthesis, Science, 228, 408, 10.1126/science.3838594 Cook, 2012, Computer-aided synthesis design: 40 years on, Wiley Interdiscip. Rev. Comput. Mol. Sci., 2, 79, 10.1002/wcms.61 Hanessian, 2005, Man, machine and visual imagery in strategic synthesis planning: computer-perceived precursors for drug candidates, Curr. Opin. Drug Discov. Dev., 8, 798 Todd, 2005, Computer-aided organic synthesis, Chem. Soc. Rev., 34, 247, 10.1039/b104620a www.infochem.de/. [Accessed 26 February 2018] Ravitz, 2013, Data-driven computer aided synthesis design, Drug Discov. Today: Technol., 10, e443, 10.1016/j.ddtec.2013.01.005 www.spresi.com/. [Accessed 26 February 2018] Bøgevig, 2015, Route design in the 21st century: the ICSYNTH software tool as an Idea generator for synthesis prediction, Org. Process Res. Dev., 19, 357, 10.1021/op500373e www.haxel.com/icic/2014/Programme/monday-13-oct-2014#knowledge-based-de-novo-molecular-design-using-icsynth-frp. [Accessed 26 February 2018] http://chematica.net/. [Accessed 26 February 2018] Szymkuć, 2016, Computer-assisted synthetic planning: the end of the beginning, Angew. Chem. Int. Ed., 55, 5904, 10.1002/anie.201506101 www.cas.org/products/scifinder-n. [Accessed 26 February 2018] CIRX. http://www.cheminform.com/reaction-library. (Accessed 7 March 2018). http://news.wiley.com/ChemPlanner_Webinar. [Accessed 26 February 2018] www.chemanager-online.com/en/whitepaper/wiley-chemplanner-predicts-experimentally-verified-synthesis-routes-medicinal-chemistry. [Accessed 26 February 2018] Bohacek, 1996, The art and practice of structure-based drug design: a molecular modeling perspective, Med. Res. Rev., 16, 3, 10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6 Deglmann, 2015, Application of quantum calculations in the chemical industry—an overview, Int. J. Quantum Chem., 115, 107, 10.1002/qua.24811 Ashley, 2017, Ruthenium-catalysed dynamic kinetic resolution asymmetric transfer hydrogenation of β-chromanones by an elimination-induced racemization mechanism, ACS Catal., 7, 1446, 10.1021/acscatal.6b03191 Cheong, 2011, Quantum mechanical investigations of organocatalysis: mechanisms, reactivities, and selectivities, Chem. Rev., 111, 5042, 10.1021/cr100212h Dirocco, 2017, A multifunctional catalyst that stereoselectively assembles prodrugs, Science, 356, 426, 10.1126/science.aam7936 Hansen, 2016, Prediction of stereochemistry using Q2MM, Acc. Chem. Res., 49, 996, 10.1021/acs.accounts.6b00037 Ji, 2017, A rational pre-catalyst design for bis-phosphine mono-oxide palladium catalysed reactions, Chem. Sci., 8, 2841, 10.1039/C6SC05472B Mccabe Dunn, 2017, The protecting-group free selective 3′-functionalization of nucleosides, Chem. Sci., 8, 2804, 10.1039/C6SC05081F Lam, 2016, Theory and modeling of asymmetric catalytic reactions, Acc. Chem. Res., 49, 750, 10.1021/acs.accounts.6b00006 Sperger, 2016, Computation and experiment: a powerful combination to understand and predict reactivities, Acc. Chem. Res., 49, 1311, 10.1021/acs.accounts.6b00068 Tantillo, 2016, Speeding up sigmatropic shifts—to halve or to hold, Acc. Chem. Res., 49, 741, 10.1021/acs.accounts.6b00029 Wheeler, 2016, Noncovalent interactions in organocatalysis and the prospect of computational catalyst design, Acc. Chem. Res., 49, 1061, 10.1021/acs.accounts.6b00096 Denmark, 2011, A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Application of quantitative structure activity/selectivity relationships, J. Org. Chem., 76, 4337, 10.1021/jo2005457 Denmark, 2012, Effects of charge separation, effective concentration, and aggregate formation on the phase transfer catalysed alkylation of phenol, J. Am. Chem. Soc., 134, 13415, 10.1021/ja304808u Harper, 2012, Multidimensional steric parameters in the analysis of asymmetric catalytic reactions, Nat. Chem., 4, 366, 10.1038/nchem.1297 Jensen, 2007, Systematically probing the effect of catalyst acidity in a hydrogen-bond-catalysed enantioselective reaction, Angew. Chem. Int. Ed., 46, 4748, 10.1002/anie.200700298 Jensen, 2010, Evaluation of catalyst acidity and substrate electronic effects in a hydrogen bond-catalysed enantioselective reaction, J. Org. Chem., 75, 7194, 10.1021/jo1013806 Jensen, 2010, Advancing the mechanistic understanding of an enantioselective palladium-catalysed alkene difunctionalization reaction, J. Am. Chem. Soc., 132, 17471, 10.1021/ja108106h Milo, 2014, Interrogating selectivity in catalysis using molecular vibrations, Nature, 507, 210, 10.1038/nature13019 Milo, 2015, Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis, Science, 347, 737, 10.1126/science.1261043 Sigman, 2016, The development of multidimensional analysis tools for asymmetric catalysis and beyond, Acc. Chem. Res., 49, 1292, 10.1021/acs.accounts.6b00194 Sigman, 2006, Ligand-modulated palladium-catalysed aerobic alcohol oxidations, Acc. Chem. Res., 39, 221, 10.1021/ar040243m Sigman, 2012, Imparting catalyst control upon classical palladium-catalysed alkenyl C-H bond functionalization reactions, Acc. Chem. Res., 45, 874, 10.1021/ar200236v Denmark, 2011, A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Synthesis of catalyst libraries and evaluation of catalyst activity, J. Org. Chem., 76, 4260, 10.1021/jo2005445 Becke, 1993, Density-functional thermochemistry. III. The role of exact exchange, J. Chem. Phys., 98, 5648, 10.1063/1.464913 Lee, 1988, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron-density, Phys. Rev. B, 37, 785, 10.1103/PhysRevB.37.785 Stephens, 1994, Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields, J. Phys. Chem., 98, 11623, 10.1021/j100096a001 Vosko, 1980, Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis, Can. J. Phys., 58, 1200, 10.1139/p80-159 Check, 2005, Progressive systematic underestimation of reaction energies by the B3LYP model as the number of C-C bonds increases: why organic chemists should use multiple DFT models for calculations involving polycarbon hydrocarbons, J. Org. Chem., 70, 9828, 10.1021/jo051545k Hansen, 2014, The thermochemistry of london dispersion-driven transition metal reactions: getting the ‘right answer for the right reason’, ChemistryOpen, 3, 177, 10.1002/open.201402017 Kruse, 2012, Why the standard B3LYP/6-31G* model chemistry should not be used in DFT calculations of molecular thermochemistry: understanding and correcting the problem, J. Org. Chem., 77, 10824, 10.1021/jo302156p Biedermann, 2016, Experimental binding energies in supramolecular complexes, Chem. Rev., 116, 5216, 10.1021/acs.chemrev.5b00583 Grimme, 2016, Dispersion-corrected mean-field electronic structure methods, Chem. Rev., 116, 5105, 10.1021/acs.chemrev.5b00533 Li, 2014, Quantum mechanical calculation of noncovalent interactions: a large-scale evaluation of PMx, DFT, and SAPT approaches, J. Chem. Theory Comput., 10, 1563, 10.1021/ct401111c Mardirossian, 2016, How accurate are the Minnesota density functionals for noncovalent interactions, isomerization energies, thermochemistry, and barrier heights involving molecules composed of main-group elements?, J. Chem. Theory Comput., 12, 4303, 10.1021/acs.jctc.6b00637 Ramakrishnan, 2014, Quantum chemistry structures and properties of 134kilo molecules, Sci. Data, 1, 140022, 10.1038/sdata.2014.22 Řezáč, 2016, Benchmark calculations of interaction energies in noncovalent complexes and their applications, Chem. Rev., 116, 5038, 10.1021/acs.chemrev.5b00526 Zheng, 2007, Representative benchmark suites for barrier heights of diverse reaction types and assessment of electronic structure methods for thermochemical kinetics, J. Chem. Theory Comput., 3, 569, 10.1021/ct600281g Chai, 2008, Long-range corrected hybrid density functionals with damped atom-atom dispersion corrections, Phys. Chem. Chem. Phys., 10, 6615, 10.1039/b810189b Zhao, 2008, Theor. Chem. Acc., 120, 215, 10.1007/s00214-007-0310-x Grimme, 2011, Effect of the damping function in dispersion corrected density functional theory, J. Comput. Chem., 32, 1456, 10.1002/jcc.21759 Ramabhadran, 2011, Theoretical thermochemistry for organic molecules: development of the generalized connectivity-based hierarchy, J. Chem. Theory Comput., 7, 2094, 10.1021/ct200279q Ramabhadran, 2014, The successful merger of theoretical thermochemistry with fragment-based methods in quantum chemistry, Acc. Chem. Res., 47, 3596, 10.1021/ar500294s Grimme, 2012, Supramolecular binding thermodynamics by dispersion-corrected density functional theory, ‎Chem. Eur. J., 18, 9955, 10.1002/chem.201200497 Ribeiro, 2011, Use of solution-phase vibrational frequencies in continuum models for the free energy of solvation, J. Phys. Chem. B, 115, 14556, 10.1021/jp205508z Harvey, 2006, On the accuracy of density functional theory in transition metal chemistry, Annu. Rep. Prog. Chem. Sect. C: Phys. Chem., 102, 203, 10.1039/b419105f Weymuth, 2014, New benchmark set of transition-metal coordination reactions for the assessment of density functionals, J. Chem. Theory Comput., 10, 3092, 10.1021/ct500248h Hopmann, 2016, How accurate is DFT for iridium-mediated chemistry?, Organometallics, 35, 3795, 10.1021/acs.organomet.6b00377 Sperger, 2015, Computational studies of synthetically relevant homogeneous organometallic catalysis involving Ni, Pd, Ir, and Rh: an overview of commonly employed DFT methods and mechanistic insights, Chem. Rev., 115, 9532, 10.1021/acs.chemrev.5b00163 Sun, 2014, Performance of density functionals for activation energies of re-catalysed organic reactions, J. Chem. Theory Comput., 10, 579, 10.1021/ct4010855 Bock, 2010, Crystal structures of proline-derived enamines, Proc. Natl. Acad. Sci. U. S. A., 107, 20636, 10.1073/pnas.1006509107 O’boyle, 2011, Open Babel: an open chemical toolbox, J. Cheminf., 3, 33, 10.1186/1758-2946-3-33 Vainio, 2007, Generating conformer ensembles using a multiobjective genetic algorithm, J. Chem. Inf. Model., 47, 2462, 10.1021/ci6005646 Perkin Elmer, 2017 Sherer, 2014, Systematic approach to conformational sampling for assigning absolute configuration using vibrational circular dichroism, J. Med. Chem., 57, 477, 10.1021/jm401600u Wavefunction, 2016 Bochevarov, 2013, Jaguar: a high-performance quantum chemistry software program with strengths in life and materials sciences, Int. J. Quantum Chem., 113, 2110, 10.1002/qua.24481 Frisch, 2016 Valiev, 2010, NWChem: a comprehensive and scalable open-source solution for large scale molecular simulations, Comput. Phys. Commun., 181, 1477, 10.1016/j.cpc.2010.04.018 Shao, 2015, Advances in molecular quantum chemistry contained in the Q-Chem 4 program package, Mol. Phys., 113, 184, 10.1080/00268976.2014.952696 Anon, 2016 Zimmerman, 2013, Reliable transition state searches integrated with the growing string method, J. Chem. Theory Comput., 9, 3043, 10.1021/ct400319w Maeda, 2013, Systematic exploration of the mechanism of chemical reactions: the global reaction route mapping (GRRM) strategy using the ADDF and AFIR methods, Phys. Chem. Chem. Phys., 15, 3683, 10.1039/c3cp44063j Guan, 2017 Bally, 2011, Quantum-chemical simulation of H-1 NMR spectra. 2. Comparison of DFT-based procedures for computing proton-proton coupling constants in organic molecules, J. Org. Chem., 76, 4818, 10.1021/jo200513q Buevich, 2016, Synergistic combination of CASE algorithms and DFT chemical shift predictions: a powerful approach for structure elucidation, verification, and revision, J. Nat. Prod., 79, 3105, 10.1021/acs.jnatprod.6b00799 Chavali, 2007, Mid IR CD spectroscopy for medicinal chemistry: a pharmaceutical perspective, Am. Pharm. Rev., 10, 94 Cheeseman, 2011, Basis set dependence of vibrational Raman and Raman optical activity intensities, J. Chem. Theory Comput., 7, 3323, 10.1021/ct200507e Freedman, 2003, Absolute configuration determination of chiral molecules in the solution state using vibrational circular dichroism, Chirality, 15, 743, 10.1002/chir.10287 He, 2011, Determination of absolute configuration of chiral molecules using vibrational optical activity: a review, Appl. Spectrosc., 65, 699, 10.1366/11-06321 Hwang, 2016, Application of 1,1-ADEQUATE, HMBC, and density functional theory to determine regioselectivity in the halogenation of pyridine N-oxides, Org. Lett., 18, 1956, 10.1021/acs.orglett.6b00370 Kutateladze, 2017, High-throughput in silico structure validation and revision of halogenated natural products is enabled by parametric corrections to DFT-computed C-13 NMR chemical shifts and spin-spin coupling constants, J. Org. Chem., 82, 3368, 10.1021/acs.joc.7b00188 Mevers, 2016, Homodimericin A: a complex hexacyclic fungal metabolite, J. Am. Chem. Soc., 138, 12324, 10.1021/jacs.6b07588 Minick, 2007, Strategies for successfully applying vibrational circular dichroism in a pharmaceutical research environment, Am. Pharm. Rev., 10, 118 Nafie, 1976, Vibrational circular-dichroism, J. Am. Chem. Soc., 98, 2715, 10.1021/ja00426a007 Navarro-Vazquez, 2017, State of the art and perspectives in the application of quantum chemical prediction of H-1 and C-13 chemical shifts and scalar couplings for structural elucidation of organic compounds, Magn. Reson. Chem., 55, 29, 10.1002/mrc.4502 Smith, 2010, Assigning stereochemistry to single diastereoisomers by GIAO NMR calculation: the DP4 probability, J. Am. Chem. Soc., 132, 12946, 10.1021/ja105035r Stephens, 2012 Willoughby, 2014, A guide to small-molecule structure assignment through computation of (H-1 and C-13) NMR chemical shifts, Nat Protoc., 9, 643, 10.1038/nprot.2014.042 Sherer, 2015, Absolute configuration of remisporines A & B, Org. Biomol. Chem., 13, 4169, 10.1039/C5OB00082C Stephens, 2004, Determination of absolute configuration using concerted ab initio DFT calculations of electronic circular dichroism and optical rotation: bicyclo[3.3.1]nonane diones, J. Org. Chem., 69, 1948, 10.1021/jo0357061 Cramer, 2017, Prediction of mass spectral response factors from predicted chemometric data for druglike molecules, J. Am. Soc. Mass. Spectrom., 28, 278, 10.1007/s13361-016-1536-4 Houk, 2017, Holy grails for computational organic chemistry and biochemistry, Acc. Chem. Res., 50, 539, 10.1021/acs.accounts.6b00532 Peverati, 2014, Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics, Philos. Trans. A Math. Phys. Eng. Sci., 372, 20120476 Jensen, 2015, Predicting accurate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods, Phys. Chem. Chem. Phys., 17, 12441, 10.1039/C5CP00628G Liu, 2017, Mechanism and reactivity in the Morita-Baylis-Hillman reaction: the challenge of accurate computations, Phys. Chem. Chem. Phys., 19, 30647, 10.1039/C7CP06508F Plata, 2015, A case study of the mechanism of alcohol-mediated Morita Baylis–Hillman reactions. The importance of experimental observations, J. Am. Chem. Soc., 137, 3811, 10.1021/ja5111392 Xu, 2011, How well can modern density functionals predict internuclear distances at transition states?, J. Chem. Theory Comput., 7, 1667, 10.1021/ct2001057 Simón, 2011, How reliable are DFT transition structures? Comparison of GGA, hybrid-meta-GGA and meta-GGA functionals, Org. Biomol. Chem., 9, 689, 10.1039/C0OB00477D Steinmetz, 2013, Benchmark study of the performance of density functional theory for bond activations with (Ni,Pd)-based transition-metal catalysts, ChemistryOpen, 2, 115, 10.1002/open.201300012 Maki, 2009, Impact of solvent polarity on N-heterocyclic carbene-catalysed beta-protonations of homoenolate equivalents, Org. Lett., 11, 3942, 10.1021/ol901545m Lowe, 2012 Kraut, 2013, Algorithm for reaction classification, J. Chem. Inf. Model., 53, 2884, 10.1021/ci400442f Verras, 2017, Shared consensus machine learning models for predicting blood stage malaria inhibition, J. Chem. Inf. Model., 57, 445, 10.1021/acs.jcim.6b00572 https://sciencebusiness.technewslit.com/?p514386. [Accessed 26 February 2018] Schütt, 2017, Quantum-chemical insights from deep tensor neural networks, Nat. Commun., 8, 13890, 10.1038/ncomms13890