Machine Learning and Big-Data in Computational Chemistry

Experimental chemistry and the younger discipline of computational chemistry have always aspired to increase data volume, velocity, and variety. The recent software developments in machine learning, databases and automation and hardware advances in fast co-processors, networking, and storage have boosted automation and digitization. Computational chemistry is seemingly on the verge of a big-data revolution.

In this chapter, we discuss how many of these data-driven paradigms are part of long-term trend and data have long been at the heart of many chemical problems. Historical repositories of chemical data where the modern cheminformatician can mine high value curated training data are reviewed. Modern automation tools and datasets available for high-data computational chemistry are described. Current applications of computer-driven discovery of molecular materials in optoelectronics (photovoltaics and light-emitting diodes) and electrical energy storage are discussed. Finally, the impact of machine learning approaches to computational chemistry areas of structure-property relationships and chemical space, with an emphasis on generative models, are analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Similar content being viewed by others

Machine Learning and Big-Data in Computational Chemistry

Machine learning in chemical reaction space

Cheminformatics: At the Crossroad of Eras

References

• Álvarez-Moreno M, de Graaf C, López N, Maseras F, Poblet JM, Bo C (2015) Managing the computational chemistry big data problem: the ioChem-BD platform. J Chem Inf Model 55:95 ArticleGoogle Scholar
• Araujo RB, Banerjee A, Panigrahi P, Yang L, Strømme M, Sjödin M, Araujo CM, Ahuja R (2017) Designing strategies to tune reduction potential of organic molecules for sustainable high capacity battery application. J Mater Chem A 5:4430 ArticleGoogle Scholar
• Behler J (2011a) Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J Chem Phys 134:74106 ArticleGoogle Scholar
• Behler J (2011b) Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys Chem Chem Phys 13:17930 ArticleGoogle Scholar
• Behler J (2017) First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angew Chemie Int Ed 56:12828 ArticleGoogle Scholar
• Behler J, Lorenz S, Reuter K (2007) Representing molecule-surface interactions with symmetry-adapted neural networks. J Chem Phys 127:14705 ArticleADSGoogle Scholar
• Behler J, Parrinello M (2007) Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett 98:146401 ArticleADSGoogle Scholar
• Belsky A, Hellenbrandt M, Karen VL, Luksch P (2002) New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. Acta Crystallogr Sect B Struct Sci 58:364 ArticleGoogle Scholar
• Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235 ArticleGoogle Scholar
• Bernstein FC, Koetzle TF, Williams GJB, Meyer EF, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1978) The protein data bank: a computer-based archival file for macromolecular structures. Arch Biochem Biophys 185:584 ArticleGoogle Scholar
• Bjerrum EJ (2017) SMILES enumeration as data augmentation for neural Network modeling of molecules Arxiv.Org Google Scholar
• Blank TB, Brown SD, Calhoun AW, Doren DJ (1995) Neural network models of potential energy surfaces. J Chem Phys 103:4129 ArticleADSGoogle Scholar
• Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H (2017) Application of generative autoencoder in de Novo molecular design Mol. Inform Google Scholar
• Block P, Sotriffer CA, Dramburg I, Klebe G (2006) AffinDB: a freely accessible database of affinities for protein-ligand complexes from the PDB. Nucleic Acids Res 34:D522 ArticleGoogle Scholar
• Blum LC, Reymond J-L (2009) 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131:8732 ArticleGoogle Scholar
• Borodin O, Olguin M, Spear CE, Leiter KW, Knap J (2015) Towards high throughput screening of electrochemical stability of battery electrolytes. Nanotechnology 26:354003 ArticleGoogle Scholar
• Brockherde F, Vogt L, Li L, Tuckerman ME, Burke K, Müller K-R (2017) Bypassing the Kohn-Sham equations with machine learning. Nat Commun 8:872 ArticleADSGoogle Scholar
• Bruno I, Gražulis S, Helliwell JR, Kabekkodu SN, McMahon B, Westbrook J (2017) Crystallography and databases. Data Sci J 16 Google Scholar
• Calderon CE, Plata JJ, Toher C, Oses C, Levy O, Fornari M, Natan A, Mehl MJ, Hart G, Buongiorno Nardelli M, Curtarolo S (2015) The AFLOW standard for high-throughput materials science calculations. Comput Mater Sci 108:233 ArticleGoogle Scholar
• Chambers J, Davies M, Gaulton A, Hersey A, Velankar S, Petryszak R, Hastings J, Bellis L, McGlinchey S, Overington JP (2013) UniChem: a unified chemical structure cross-referencing and identifier tracking system. J Cheminform 5:3 ArticleGoogle Scholar
• Chen X, Liu M, Gilson MK (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4:719 ArticleGoogle Scholar
• Cheng L, Assary RS, Qu X, Jain A, Ong SP, Rajput NN, Persson K, Curtiss LA (2015) Accelerating electrolyte discovery for energy storage with high-throughput screening. J Phys Chem Lett 6:283 ArticleGoogle Scholar
• Chmiela S, Tkatchenko A, Sauceda HE, Poltavsky I, Schütt KT, Müller K-R (2017) Machine learning of accurate energy-conserving molecular force fields. Sci Adv 3:e1603015 ArticleADSGoogle Scholar
• Coley CW, Barzilay R, Jaakkola TS, Green WH, Jensen KF (2017a) Prediction of organic reaction outcomes using machine learning. ACS Cent Sci 3:434 ArticleGoogle Scholar
• Coley CW, Rogers L, Green WH, Jensen KF (2017b) Computer-assisted retrosynthesis based on molecular similarity. ACS Cent Sci 3:1237 ArticleGoogle Scholar
• Curtarolo S, Setyawan W, Hart GLW, Jahnatek M, Chepulskii RV, Taylor RH, Wang S, Xue J, Yang K, Levy O, Mehl MJ, Stokes HT, Demchenko DO, Morgan D (2012a) AFLOW: an automatic framework for high-throughput materials discovery. Comput Mater Sci 58:218 ArticleGoogle Scholar
• Curtarolo S, Setyawan W, Wang S, Xue J, Yang K, Taylor RH, Nelson LJ, Hart GLW, Sanvito S, Buongiorno-Nardelli M, Mingo N, Levy O (2012b) AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput Mater Sci 58:227 ArticleGoogle Scholar
• Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, Wiegers J, Wiegers TC, Mattingly CJ (2017) The comparative toxicogenomics database: update 2017. Nucleic Acids Res 45:D972 ArticleGoogle Scholar
• Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, Alcántara R, Darsow M, Guedj M, Ashburner M (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36:D344 ArticleGoogle Scholar
• Ding H, Medasani B, Chen W, Persson KA, Haranczyk M, Asta M (2015) PyDII: a python framework for computing equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallic compounds. Comput Phys Commun 193:118 ArticleADSGoogle Scholar
• Duvenaud DK, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Adv Neural Inf Process Syst 2:2215–2223 Google Scholar
• Elward JM, Rinderspacher BC (2015) Smooth heuristic optimization on a complex chemical subspace. Phys Chem Chem Phys 17:24322 ArticleGoogle Scholar
• Er S, Suh C, Marshak MP, Aspuru-Guzik A (2015) Computational design of molecules for an all-quinone redox flow battery. Chem Sci 6:885 ArticleGoogle Scholar
• Ertl P, Lewis R, Martin E, Polyakov V (2017) In silico generation of novel, drug-like chemical matter using the LSTM neural network Arxiv.Org Google Scholar
• Faber J, Fawcett T, IUCr (2002) The powder diffraction file: present and future. Acta Crystallogr Sect B Struct Sci 58:325 ArticleGoogle Scholar
• Feller D (1996) The role of databases in support of computational chemistry calculations. J Comput Chem 17:1571 ArticleGoogle Scholar
• Fink T, Reymond JL (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discove. J Chem Inf Model 47:342 ArticleGoogle Scholar
• Fooshee D, Mood A, Gutman E, Tavakoli M, Urban G, Liu F, Huynh N, Van Vranken D, Baldi P (2018) Deep learning for chemical reaction prediction. Mol Syst Des Eng Google Scholar
• Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100 ArticleGoogle Scholar
• Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. arXiv1704.01212 [Cs] Google Scholar
• Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045 ArticleGoogle Scholar
• Goldsmith B (2016) NoMaD repository entry Google Scholar
• Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D, Blood-Forsythe MA, Chae HS, Einzinger M, Ha D-G, Wu T, Markopoulos G, Jeon S, Kang H, Miyazaki H, Numata M, Kim S, Huang W, Hong SI, Baldo M, Adams RP, Aspuru-Guzik A (2016) Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater 15:1120 ArticleADSGoogle Scholar
• Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4:268 ArticleGoogle Scholar
• Goyal A, Gorai P, Peng H, Lany S, Stevanović V (2017) A computational framework for automation of point defect calculations. Comput Mater Sci 130:1 ArticleGoogle Scholar
• Gražulis S, Chateigner D, Downs RT, Yokochi AFT, Quirós M, Lutterotti L, Manakova E, Butkus J, Moeck P, Le Bail A (2009) Crystallography open database – an open-access collection of crystal structures. J Appl Crystallogr 42:726 ArticleGoogle Scholar
• Gražulis S, Daškevič A, Merkys A, Chateigner D, Lutterotti L, Quirós M, Serebryanaya NR, Moeck P, Downs RT, Le Bail A (2012) Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration. Nucleic Acids Res 40:D420 ArticleGoogle Scholar
• Griffiths R-R, Hernández-Lobato JM (2017) Constrained bayesian optimization for automatic chemical design. ArXiv:1709.05501 Google Scholar
• Groom CR, Bruno IJ, Lightfoot MP, Ward SC, IUCr (2016) The Cambridge structural database. Acta Crystallogr Sect B Struct Sci Cryst Eng Mater 72:171 ArticleGoogle Scholar
• Guimaraes GL, Sanchez-Lengeling B, Outeiral C, Farias PLC, Aspuru-Guzik A (2017) Objective-Reinforced Generative Adversarial Networks (ORGAN) for sequence generation models. ArXiv:1705.10843 Google Scholar
• Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2017) Generative recurrent networks for De Novo drug design. Mol Inf Google Scholar
• Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, Amador-Bedolla C, Sanchez-Carrera RS, Gold-Parker A, Vogt L, Brockway AM, Aspuru-Guzik A (2011) The Harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett 2:2241 ArticleGoogle Scholar
• Hachmann J, Olivares-Amaya R, Jinich A, Appleton AL, Blood-Forsythe MA, Seress LR, Roman-Salgado C, Trepte K, Atahan-Evrenk S, Er S, Shrestha S, Mondal R, Sokolov A, Bao Z, Aspuru-Guzik A (2014) Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project. Energy Environ Sci 7:698 ArticleGoogle Scholar
• Heifets A, Jurisica I (2012) SCRIPDB: a portal for easy access to syntheses, chemicals and reactions in patents. Nucleic Acids Res 40:D428 ArticleGoogle Scholar
• Hermann G, Pohl V, Tremblay JC, Paulus B, Hege H-C, Schild A (2016) ORBKIT: a modular python toolbox for cross-platform postprocessing of quantum chemical wavefunction data. J Comput Chem 37:1511 ArticleGoogle Scholar
• Hjorth Larsen A, Jørgen Mortensen J, Blomqvist J, Castelli IE, Christensen R, Dułak M, Friis J, Groves MN, Hammer B, Hargus C, Hermes ED, Jennings PC, Bjerre Jensen P, Kermode J, Kitchin JR, Leonhard Kolsbjerg E, Kubal J, Kaasbjerg K, Lysgaard S, Bergmann Maronsson J, Maxson T, Olsen T, Pastewka L, Peterson A, Rostgaard C, Schiøtz J, Schütt O, Strange M, Thygesen KS, Vegge T, Vilhelmsen L, Walter M, Zeng Z, Jacobsen KW (2017) The atomic simulation environment – a Python library for working with atoms. J Phys Condens Matter 29:273002 ArticleGoogle Scholar
• Holliday GL, Bartlett GJ, Almonacid DE, O’Boyle NM, Murray-Rust P, Thornton JM, Mitchell JBO (2005) MACiE: a database of enzyme reaction mechanisms. Bioinformatics 21:4315 ArticleGoogle Scholar
• Huskinson B, Marshak MP, Suh C, Er S, Gerhardt MR, Galvin CJ, Chen X, Aspuru-Guzik A, Gordon RG, Aziz MJ (2014) A metal-free organic-inorganic aqueous flow battery. Nature 505:195 ArticleADSGoogle Scholar
• Russel D. Johnson II (1999) Computational chemistry comparison and benchmark database. NIST Standard Reference Database Number 101 Release 18, Oct 2016 Google Scholar
• Jacob CR, Beyhan SM, Bulo RE, Gomes ASP, Götz AW, Kiewisch K, Sikkema J, Visscher L (2011) PyADF - A scripting framework for multiscale quantum chemistry. J Comput Chem 32:2328 ArticleGoogle Scholar
• Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson KA (2013) Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 1:11002 ArticleADSGoogle Scholar
• Janz D, van der Westhuizen J, Hernández-Lobato JM (2017) Actively learning what makes a discrete sequence valid. ArXiv:1708.04465 Google Scholar
• Jaques N, Gu S, Bahdanau D, Hernández-Lobato JM, Turner RE, Eck D (2016) Sequence Tutor: conservative Fine-Tuning of Sequence Generation Models with KL-control Proceedings.Mlr.Press Google Scholar
• Jin W, Coley C, Barzilay R, Jaakkola T (2017) Predicting organic reaction outcomes with Weisfeiler-Lehman network ArXiv:1709.04555 2604 Google Scholar
• Kaiser J (2005) Science resources. Chemists want NIH to curtail database. Science 308:774 ArticleGoogle Scholar
• Kanal IY, Hutchison GR (2017) Rapid computational optimization of molecular properties using genetic algorithms: searching across millions of compounds for organic photovoltaic materials ArXiv:1707.02949 [Physics] Google Scholar
• Karpathy A (2015) Google Scholar
• Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30:595 ArticleADSGoogle Scholar
• Killoran N, Lee LJ, Delong A, Duvenaud D, Frey BJ (2017) Generating and designing DNA with deep generative models ArXiv:1712.06148 Google Scholar
• Kirklin S, Saal JE, Meredig B, Thompson A, Doak JW, Aykol M, Rühl S, Wolverton C (2015) The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. Npj Comput Mater 1:15010 ArticleADSGoogle Scholar
• Klintenberg M, Derenzo SE, Weber MJ (2002) Potential scintillators identified by electronic structure calculations. Nucl Instruments Methods Phys Res Sect A Accel Spectrometers, Detect Assoc Equip 486:298 ArticleADSGoogle Scholar
• Kowalski JA, Su L, Milshtein JD, Brushett FR (2016) Recent advances in molecular engineering of redox active organic molecules for nonaqueous flow batteries. Curr Opin Chem Eng 13:45 ArticleGoogle Scholar
• Kusner MJ, Paige B, Hernández-Lobato JM (2017) Grammar variational autoencoder arXiv:1703.01925 [Stat] Google Scholar
• Landis DD, Hummelshoj JS, Nestorov S, Greeley J, Dulak M, Bligaard T, Norskov JK, Jacobsen KW (2012) The Computational materials repository. Comput Sci Eng 14:51 ArticleGoogle Scholar
• Leung P, Shah AA, Sanz L, Flox C, Morante JR, Xu Q, Mohamed MR, Ponce de León C, Walsh FC (2017) Recent developments in organic redox flow batteries: a critical review. J Power Sources 360:243 ArticleGoogle Scholar
• Lin L (2015) Materials databases infrastructure constructed by first principles calculations: a review. Mater Perform Charact 4:MPC20150014 ArticleGoogle Scholar
• Lin K, Gómez-Bombarelli R, Beh ES, Tong L, Chen Q, Valle A, Aspuru-Guzik A, Aziz MJ, Gordon RG (2016) A redox-flow battery with an alloxazine-based organic electrolyte. Nat Energy 1:16102 ArticleADSGoogle Scholar
• Linstrom PJ, Mallard WG (2001) The NIST Chemistry WebBook: a chemical data resource on the Internet. J Chem Eng Data 46:1059 ArticleGoogle Scholar
• Liu Z, Li Y, Han L, Li J, Liu J, Zhao Z, Nie W, Liu Y, Wang R (2015) PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics 31:405 ArticleGoogle Scholar
• Lopez SA, Pyzer-Knapp EO, Simm GN, Lutzow T, Li K, Seress LR, Hachmann J, Aspuru-Guzik A (2016) The Harvard organic photovoltaic dataset. Sci Data 3:160086 ArticleGoogle Scholar
• Lopez SA, Sanchez-Lengeling B, de Goes Soares J, Aspuru-Guzik A (2017) Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics. Joule 1:857 ArticleGoogle Scholar
• Lowe DM (2012) Extraction of chemical structures and reactions from the literature. PhD Thesis, Cambridge University, PhD.35691, https://doi.org/10.17863/CAM.16293
• Lubbers N, Smith JS, Barros K (2017) Hierarchical modeling of molecular energies using a deep neural network. J Chem Phys 148:241715 ArticleADSGoogle Scholar
• Martsinovich N, Troisi A (2011) High-throughput computational screening of chromophores for dye-sensitized solar cells. J Phys Chem C 115:11781 ArticleGoogle Scholar
• Mattingly CJ, Colby GT, Forrest JN, Boyer JL (2003) The Comparative Toxicogenomics Database (CTD). Environ Health Perspect 111:793 ArticleGoogle Scholar
• Mayeshiba T, Wu H, Angsten T, Kaczmarowski A, Song Z, Jenness G, Xie W, Morgan D (2017) The MAterials Simulation Toolkit (MAST) for atomistic modeling of defects and diffusion. Comput Mater Sci 126:90 ArticleGoogle Scholar
• Merkys A, Mounet N, Cepellotti A, Marzari N, Gražulis S, Pizzi G (2017) A posteriori metadata from automated provenance tracking: integration of AiiDA and TCOD ArXiv:1706.08704 Google Scholar
• Meyer EF (1997) The first years of the Protein Data Bank. Protein Sci 6:1591 ArticleGoogle Scholar
• Mueller J, Gifford D, Jaakkola T (2017) Sequence to better sequence: continuous revision of combinatorial structures. ICML 70:2536 Google Scholar
• Nakata M, Shimazaki T (2017) PubChemQC Project: a large-scale first-principles electronic structure database for data-driven chemistry. J Chem Inf Model 57:1300 ArticleGoogle Scholar
• Nath SR, Kurup SS, Joshi KA (2016) PyGlobal: a toolkit for automated compilation of DFT-based descriptors. J Comput Chem 37:1505 ArticleGoogle Scholar
• O’boyle NM, Tenderholt AL, Langner KM (2008) cclib: a library for package-independent computational chemistry algorithms. J Comput Chem 29:839 ArticleGoogle Scholar
• Olivares-Amaya R, Amador-Bedolla C, Hachmann J, Atahan-Evrenk S, Sanchez-Carrera RS, Vogt L, Aspuru-Guzik A (2011) Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy Environ Sci 4:4849 ArticleGoogle Scholar
• Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular De Novo Design through Deep Reinforcement Learning Arxiv.Org Google Scholar
• Ong SP, Richards WD, Jain A, Hautier G, Kocher M, Cholia S, Gunter D, Chevrier VL, Persson KA, Ceder G (2013) Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis Comput. Comput Mater Sci 68:314 ArticleGoogle Scholar
• Ong SP, Cholia S, Jain A, Brafman M, Gunter D, Ceder G, Persson KA (2015) The Materials Application Programming Interface (API): a simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles. Comput Mater Sci 97:209 ArticleGoogle Scholar
• Ørnsø KB, Pedersen CS, Garcia-Lastra JM, Thygesen KS (2014) Optimizing porphyrins for dye sensitized solar cells using large-scale ab initio calculations. Phys Chem Chem Phys 16:16246 ArticleGoogle Scholar
• Ortiz C, Eriksson O, Klintenberg M (2009) Data mining and accelerated electronic structure theory as a tool in the search for new functional materials Comput. Comput Mater Sci 44:1042 ArticleGoogle Scholar
• Pampel H, Vierkant P, Scholze F, Bertelmann R, Kindling M, Klump J, Goebelbecker H-J, Gundlach J, Schirmbacher P, Dierolf U (2013) Making research data repositories visible: the re3data.org Registry. PLoS One 8:e78080 ArticleADSGoogle Scholar
• Papadatos G, Davies M, Dedman N, Chambers J, Gaulton A, Siddle J, Koks R, Irvine SA, Pettersson J, Goncharoff N, Hersey A, Overington JP (2016) SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res 44:D1220 ArticleGoogle Scholar
• Park MH, Lee YS, Lee H, Han Y-K (2011) Low Li+ binding affinity: an important characteristic for additives to form solid electrolyte interphases in Li-ion batteries. J Power Sources 196:5109 ArticleGoogle Scholar
• Park M-S, Kang Y-S, Im D (2015) A high-speed screening method by combining a high-throughput method and a machine-learning algorithm for developing novel organic electrolytes in rechargeable batteries. ECS Trans 68:75 ArticleGoogle Scholar
• Park MS, Park I, Kang Y-S, Im D, Doo S-G, Sik Park M, Park I, Kang Y-S, Im D, Doo S-G (2016) A search map for organic additives and solvents applicable in high-voltage rechargeable batteries. Phys Chem Chem Phys 18:26807 ArticleGoogle Scholar
• Pelzer KM, Cheng L, Curtiss LA (2017) Effects of functional groups in redox-active organic molecules: a high-throughput screening approach. J Phys Chem C 121:237 ArticleGoogle Scholar
• Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87:1123 ArticleGoogle Scholar
• Pierce TH, Hohne BA (eds) (1986) Artificial intelligence applications in chemistry (American Chemical Society). Washington, DC Google Scholar
• Pineda Flores SD, Martin-Noble GC, Phillips RL, Schrier J (2015) Bio-inspired electroactive organic molecules for aqueous redox flow batteries. 1 Thiophenoquinones. J Phys Chem C 119:21800 ArticleGoogle Scholar
• Pizzi G, Cepellotti A, Sabatini R, Marzari N, Kozinsky B (2016) AiiDA: automated interactive infrastructure and database for computational science. Comput Mater Sci 111:218 ArticleGoogle Scholar
• Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27:675 ArticleADSGoogle Scholar
• Pyzer-Knapp EO, Suh C, Gómez-Bombarelli R, Aguilera-Iparraguirre J, Aspuru-Guzik AA, Gomez-Bombarelli R, Aguilera-Iparraguirre J, Aspuru-Guzik AA, Clarke DR (2015) What is high-throughput virtual screening? a perspective from organic materials discovery. Annu Rev Mater Res 45:195 ArticleADSGoogle Scholar
• Pyzer-Knapp EO, Simm GN, Aspuru Guzik A (2016) A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials. Mater Horizons 3:226 ArticleGoogle Scholar
• Qu X, Jain A, Rajput NN, Cheng L, Zhang Y, Ong SP, Brafman M, Maginn E, Curtiss LA, Persson KA (2015) The Electrolyte Genome project: a big data approach in battery materials discovery. Comput Mater Sci 103:56 ArticleGoogle Scholar
• Qu X, Zhang Y, Rajput NN, Jain A, Maginn E, Persson KA (2017) Computational design of new magnesium electrolytes with improved properties. J Phys Chem C 121:16126 ArticleGoogle Scholar
• Ramakrishnan R, Dral PO, Rupp M, von Lilienfeld OA (2014) Quantum chemistry structures and properties of 134 kilo molecules. Sci Data 1:140022 ArticleGoogle Scholar
• Reymond J-L (2015) The chemical space project. Acc Chem Res 48:722 ArticleGoogle Scholar
• Ruddigkeit L, van Deursen R, Blum LC, Reymond J-L (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 52:2864 ArticleGoogle Scholar
• Rupakheti C, Virshup A, Yang W, Beratan DN (2015) Strategy to discover diverse optimal molecules in the small molecule universe. J Chem Inf Model 55:529 ArticleGoogle Scholar
• Rupakheti C, Al-Saadon R, Zhang Y, Virshup AM, Zhang P, Yang W, Beratan DN (2016) Diverse optimal molecular libraries for organic light-emitting diodes. J Chem Theory Comput 12:1942 ArticleGoogle Scholar
• Rupp M, Tkatchenko A, Müller K-R, von Lilienfeld OA (2012) Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett 108:58301 ArticleADSGoogle Scholar
• Sanchez-Lengeling B, Outeiral C, Guimaraes GL, Aspuru-Guzik A (2017) Optimizing distributions over molecular space. An objective-Reinforced generative adversarial network for inverse-design chemistry (ORGANIC) ChemRxiv 1 Google Scholar
• Schuchardt KL, Didier BT, Elsethagen T, Sun L, Gurumoorthi V, Chase J, Li J, Windus TL (2007) Basis set exchange: a community database for computational sciences. J Chem Inf Model 47:1045 ArticleGoogle Scholar
• Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun 8:13890 ArticleADSGoogle Scholar
• Schütter C, Husch T, Viswanathan V, Passerini S, Balducci A, Korth M (2016) Rational design of new electrolyte materials for electrochemical double layer capacitors. J Power Sources 326:541 ArticleGoogle Scholar
• Schwaller P, Gaudin T, Lanyi D, Bekas C, Laino T (2017) Found in translation: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. arXiv:1711.04810 Google Scholar
• Segler MHS, Waller MP (2017a) Modelling chemical reasoning to predict and invent reactions. Chem A Eur J 23:6118 ArticleGoogle Scholar
• Segler MHS, Waller MP (2017b) Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chem A Eur J 23:5966 ArticleGoogle Scholar
• Segler MHS, Preuss M, Waller MP (2017) Learning to plan chemical syntheses ArXiv:1708.04202 Google Scholar
• Shin Y, Liu J, Quigley JJ, Luo H, Lin X (2014) Combinatorial design of copolymer donor materials for bulk heterojunction solar cells. ACS Nano 8:6089 ArticleGoogle Scholar
• Shu Y, Levine BG (2015) Simulated evolution of fluorophores for light emitting diodes. J Chem Phys 142:104104 ArticleADSGoogle Scholar
• Sinai S, Kelsic E, Church GM, Nowak MA (2017) Variational auto-encoding of protein sequences. Arxiv.org 1 Google Scholar
• Smith JS, Isayev O, Roitberg AE (2017) ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules. Sci Data 4:170193 ArticleGoogle Scholar
• Snyder JC, Rupp M, Hansen K, Müller K-R, Burke K (2012) Finding density functionals with machine learning. Phys Rev Lett 108:253002 ArticleADSGoogle Scholar
• Teunissen JL, De Proft F, De Vleeschouwer F (2017) Tuning the HOMO-LUMO energy gap of small diamondoids using inverse molecular design. J Chem Theory Comput 13:1351 ArticleGoogle Scholar
• Thygesen KS, Jacobsen KW (2016) Making the most of materials computations. Science 354:180 ArticleADSGoogle Scholar
• van Deursen R, Reymond J-L (2007) Chemical space travel. Chem Med Chem 2:636 ArticleGoogle Scholar
• Vanderveen JR, Patiny L, Chalifoux CB, Jessop MJ, Jessop PG, Vanderveen JR, Patiny L, Chalifoux CB, Jessop MJ, Jessop PG (2015) A virtual screening approach to identifying the greenest compound for a task: application to switchable-hydrophilicity solvents. Green Chem 17:5182 ArticleGoogle Scholar
• Virshup AM, Contreras-García J, Wipf P, Yang W, Beratan DN (2013) Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-Like compounds. J Am Chem Soc 135:7296 ArticleGoogle Scholar
• Voss C (2015) Modeling molecules with recurrent neural networks Google Scholar
• Waller MP, Dresselhaus T, Yang J (2013) JACOB: an enterprise framework for computational chemistry. J Comput Chem 34:1420 ArticleGoogle Scholar
• Wang S (2017) Seq2seq Fingerprint: an unsupervised deep molecular embedding for drug discovery. Dl.acm.org 285 Google Scholar
• Wang R, Fang X, Lu Y, Wang S (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem 47:2977 ArticleGoogle Scholar
• Ward AL, Doris SE, Li L, Hughes MA, Qu X, Persson KA, Helms BA (2017) Materials genomics screens for adaptive ion transport behavior by redox-Switchable microporous polymer membranes in lithium–Sulfur batteries. ACS Cent Sci 3:399 ArticleGoogle Scholar
• Wei JN, Duvenaud D, Aspuru-Guzik A (2016) Neural networks for the prediction of organic chemistry reactions. ACS Cent Sci 2:725 ArticleGoogle Scholar
• Wei X, Pan W, Duan W, Hollas A, Yang Z, Li B, Nie Z, Liu J, Reed D, Wang W, Sprenkle V (2017) Materials and systems for organic redox flow batteries: status and challenges. ACS Energy Lett 2:2187 ArticleGoogle Scholar
• Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Model 28:31 ArticleGoogle Scholar
• Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668 ArticleGoogle Scholar
• Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074 ArticleGoogle Scholar
• Yuan G, Gygi F (2010) ESTEST: a framework for the validation and verification of electronic structure codes. Comput Sci Discov 3:15004 ArticleGoogle Scholar

Acknowledgments

AAG acknowledges support from The Department of Energy, Office of Basic Energy Sciences under award de-sc0015959. He also thanks Dr. Anders Frøseth for his generous support of this work. RGB acknowledges the Toyota Career Development Chair for financial support.