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1.
1.A. Jahan, M. Ismail, S. Sapuan, and F. Mustapha, “Material screening and choosing methods—A review,” Mater. Des. 31, 696705 (2010).
http://dx.doi.org/10.1016/j.matdes.2009.08.013
2.
2.R. Potyrailo, K. Rajan, K. Stoewe, I. Takeuchi, B. Chisholm, and H. Lam, “Combinatorial and high-throughput screening of materials libraries: Review of state of the art,” ACS Comb. Sci. 13, 579633 (2011).
http://dx.doi.org/10.1021/co200007w
3.
3.R. S. Bohacek, C. McMartin, and W. C. Guida, “The art and practice of structure-based drug design: A molecular modeling perspective,” Med. Res. Rev. 16, 350 (1996).
http://dx.doi.org/10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6
4.
4.C. M. Dobson, “Chemical space and biology,” Nature 432, 824828 (2004).
http://dx.doi.org/10.1038/nature03192
5.
5.C. Lipinski and A. Hopkins, “Navigating chemical space for biology and medicine,” Nature 432, 855861 (2004).
http://dx.doi.org/10.1038/nature03193
6.
6.M. A. Koch, A. Schuffenhauer, M. Scheck, S. Wetzel, M. Casaulta, A. Odermatt, P. Ertl, and H. Waldmann, “Charting biologically relevant chemical space: A structural classification of natural products (SCONP),” Proc. Natl. Acad. Sci. U. S. A. 102, 1727217277 (2005).
http://dx.doi.org/10.1073/pnas.0503647102
7.
7.P. M. Dean, Molecular Similarity in Drug Design (Springer Science and Business Media, 2012).
8.
8.T. I. Oprea and J. Gottfries, “Chemography: The art of navigating in chemical space,” J. Comb. Chem. 3, 157166 (2001).
http://dx.doi.org/10.1021/cc0000388
9.
9.R. E. Newnham, Structure-Property Relations (Springer Science and Business Media, 2012), Vol. 2.
10.
10.G. M. Maggiora, “On outliers and activity Cliffs why QSAR often disappoints,” J. Chem. Inf. Model. 46, 1535 (2006).
http://dx.doi.org/10.1021/ci060117s
11.
11.I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2005).
12.
12.C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).
13.
13.H. Meuer, E. Strohmaier, J. Dongarra, and H. Simon, Top500 supercomputing sites,2011.
14.
14.S. H. Fuller and L. I. Millett, “Computing performance: Game over or next level?,” Computer 44, 3138 (2011).
http://dx.doi.org/10.1109/MC.2011.15
15.
15.M. Vendruscolo and C. M. Dobson, “Protein dynamics: Moore’s law in molecular biology,” Curr. Biol. 21, R68R70 (2011).
http://dx.doi.org/10.1016/j.cub.2010.11.062
16.
16.J. C. Snyder, M. Rupp, K. Hansen, K.-R. Müller, and K. Burke, “Finding density functionals with machine learning,” Phys. Rev. Lett. 108, 253002 (2012).
http://dx.doi.org/10.1103/PhysRevLett.108.253002
17.
17.M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld, “Fast and accurate modeling of molecular atomization energies with machine learning,” Phys. Rev. Lett. 108, 058301 (2012).
http://dx.doi.org/10.1103/PhysRevLett.108.058301
18.
18.K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. A. von Lilienfeld, A. Tkatchenko, and K.-R. Müller, “Assessment and validation of machine learning methods for predicting molecular atomization energies,” J. Chem. Theory Comput. 9, 34043419 (2013).
http://dx.doi.org/10.1021/ct400195d
19.
19.P.-G. De Gennes and J. Badoz, Fragile Objects: Soft Matter, Hard Science, and the Thrill of Discovery (Springer Science and Business Media, 2012).
20.
20.T. Schlick, R. D. Skeel, A. T. Brunger, L. V. Kalé, J. A. Board, J. Hermans, and K. Schulten, “Algorithmic challenges in computational molecular biophysics,” J. Comput. Phys. 151, 948 (1999).
http://dx.doi.org/10.1006/jcph.1998.6182
21.
21.B. Palsson et al., “The challenges of in silico biology,” Nat. Biotechnol. 18, 11471150 (2000).
http://dx.doi.org/10.1038/81125
22.
22.K. M. Merz and B. Roux, Biological Membranes: A Molecular Perspective from Computation and Experiment (Springer Science and Business Media, 2012).
23.
23.R. O. Dror, R. M. Dirks, J. Grossman, H. Xu, and D. E. Shaw, “Biomolecular simulation: A computational microscope for molecular biology,” Ann. Rev. Biophys. 41, 429452 (2012).
http://dx.doi.org/10.1146/annurev-biophys-042910-155245
24.
24.T. J. Lane, D. Shukla, K. A. Beauchamp, and V. S. Pande, “To milliseconds and beyond: Challenges in the simulation of protein folding,” Curr. Opin. Struct. Biol. 23, 5865 (2013).
http://dx.doi.org/10.1016/j.sbi.2012.11.002
25.
25.J. R. Perilla, B. C. Goh, C. K. Cassidy, B. Liu, R. C. Bernardi, T. Rudack, H. Yu, Z. Wu, and K. Schulten, “Molecular dynamics simulations of large macromolecular complexes,” Curr. Opin. Struct. Biol. 31, 6474 (2015).
http://dx.doi.org/10.1016/j.sbi.2015.03.007
26.
26.S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R. H. Taylor, L. J. Nelson, G. L. Hart, S. Sanvito, M. Buongiorno-Nardelli et al., “Aflowlib.org: A distributed materials properties repository from high-throughput ab initio calculations,” Comput. Mater. Sci. 58, 227235 (2012).
http://dx.doi.org/10.1016/j.commatsci.2012.02.002
27.
27.S. Curtarolo, D. Morgan, K. Persson, J. Rodgers, and G. Ceder, “Predicting crystal structures with data mining of quantum calculations,” Phys. Rev. Lett. 91, 135503 (2003).
http://dx.doi.org/10.1103/PhysRevLett.91.135503
28.
28.B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, “Combinatorial screening for new materials in unconstrained composition space with machine learning,” Phys. Rev. B 89, 094104 (2014).
http://dx.doi.org/10.1103/PhysRevB.89.094104
29.
29.J. Greeley, T. F. Jaramillo, J. Bonde, I. Chorkendorff, and J. K. Nørskov, “Computational high-throughput screening of electrocatalytic materials for hydrogen evolution,” Nat. Mater. 5, 909913 (2006).
http://dx.doi.org/10.1038/nmat1752
30.
30.M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids (Oxford University Press, 1989).
31.
31.D. P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics (Cambridge University Press, 2014).
32.
32.R. B. Best, N.-V. Buchete, and G. Hummer, “Are current molecular dynamics force fields too helical?,” Biophys. J. 95, L07L09 (2008).
http://dx.doi.org/10.1529/biophysj.108.132696
33.
33.K. Lindorff-Larsen, P. Maragakis, S. Piana, M. P. Eastwood, R. O. Dror, and D. E. Shaw, “Systematic validation of protein force fields against experimental data,” PLoS One 7, e32131 (2012).
http://dx.doi.org/10.1371/journal.pone.0032131
34.
34.S. Piana, J. L. Klepeis, and D. E. Shaw, “Assessing the accuracy of physical models used in protein-folding simulations: Quantitative evidence from long molecular dynamics simulations,” Curr. Opin. Struct. Biol. 24, 98105 (2014).
http://dx.doi.org/10.1016/j.sbi.2013.12.006
35.
35.A. Botan, F. Favela-Rosales, P. F. Fuchs, M. Javanainen, M. Kanduc, W. Kulig, A. Lamberg, C. Loison, A. Lyubartsev, M. S. Miettinen et al., “Toward atomistic resolution structure of phosphatidylcholine headgroup and glycerol backbone at different ambient conditions,” J. Phys. Chem. B 119, 1507515088 (2015).
http://dx.doi.org/10.1021/acs.jpcb.5b04878
36.
36.C. Neale, W. D. Bennett, D. P. Tieleman, and R. Pomès, “Statistical convergence of equilibrium properties in simulations of molecular solutes embedded in lipid bilayers,” J. Chem. Theory Comput. 7, 41754188 (2011).
http://dx.doi.org/10.1021/ct200316w
37.
37.C. Kutzner, R. Apostolov, B. Hess, and H. Grubmüller, “Scaling of the gromacs 4.6 molecular dynamics code on superMUC,” in Advances in Parallel Computing (IOS Press, 2013), Vol. 25.
38.
38.C. Kutzner, S. Páll, M. Fechner, A. Esztermann, B. L. de Groot, and H. Grubmüller, “Best bang for your buck: GPU nodes for gromacs biomolecular simulations,” J. Comput. Chem. 36, 19902008 (2015).
http://dx.doi.org/10.1002/jcc.24030
39.
39.A. C. Pan, D. W. Borhani, R. O. Dror, and D. E. Shaw, “Molecular determinants of drug–receptor binding kinetics,” Drug Discovery Today 18, 667673 (2013).
http://dx.doi.org/10.1016/j.drudis.2013.02.007
40.
40.R. O. Dror, H. F. Green, C. Valant, D. W. Borhani, J. R. Valcourt, A. C. Pan, D. H. Arlow, M. Canals, J. R. Lane, R. Rahmani et al., “Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs,” Nature 503, 295299 (2013).
http://dx.doi.org/10.1038/nature12595
41.
41.J. E. Stone, D. J. Hardy, I. S. Ufimtsev, and K. Schulten, “GPU-accelerated molecular modeling coming of age,” J. Mol. Graphics Modell. 29, 116125 (2010).
http://dx.doi.org/10.1016/j.jmgm.2010.06.010
42.
42.R. B. Best, “Atomistic molecular simulations of protein folding,” Curr. Opin. Struct. Biol. 22, 5261 (2012).
http://dx.doi.org/10.1016/j.sbi.2011.12.001
43.
43.V. A. Voelz, G. R. Bowman, K. Beauchamp, and V. S. Pande, “Molecular simulation of ab initio protein folding for a millisecond folder NTL9 (1-39),” J. Am. Chem. Soc. 132, 15261528 (2010).
http://dx.doi.org/10.1021/ja9090353
44.
44.J. D. Chodera and F. Noé, “Markov state models of biomolecular conformational dynamics,” Curr. Opin. Struct. Biol. 25, 135144 (2014).
http://dx.doi.org/10.1016/j.sbi.2014.04.002
45.
45.N. Plattner and F. Noé, “Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models,” Nat. Commun. 6, 7653 (2015).
http://dx.doi.org/10.1038/ncomms8653
46.
46.K. H. Bleicher, H.-J. Böhm, K. Müller, and A. I. Alanine, “Hit and lead generation: Beyond high-throughput screening,” Nat. Rev. Drug Discovery 2, 369378 (2003).
http://dx.doi.org/10.1038/nrd1086
47.
47.G. Schneider and U. Fechner, “Computer-based de novo design of drug-like molecules,” Nat. Rev. Drug Discovery 4, 649663 (2005).
http://dx.doi.org/10.1038/nrd1799
48.
48.G. M. Keserű and G. M. Makara, “Hit discovery and hit-to-lead approaches,” Drug Discovery Today 11, 741748 (2006).
http://dx.doi.org/10.1016/j.drudis.2006.06.016
49.
49.E. Kerns and L. Di, Drug-Like Properties: Concepts, Structure Design and Methods: From ADME to Toxicity Optimization (Academic Press, 2010).
50.
50.J. D. Chodera, D. L. Mobley, M. R. Shirts, R. W. Dixon, K. Branson, and V. S. Pande, “Alchemical free energy methods for drug discovery: Progress and challenges,” Curr. Opin. Struct. Biol. 21, 150160 (2011).
http://dx.doi.org/10.1016/j.sbi.2011.01.011
51.
51.W. L. Jorgensen, “The many roles of computation in drug discovery,” Science 303, 18131818 (2004).
http://dx.doi.org/10.1126/science.1096361
52.
52.J. Zupan and J. Gasteiger, Neural Networks in Chemistry and Drug Design (John Wiley & Sons, Inc., 1999).
53.
53.G. Schneider, “Virtual screening: An endless staircase?,” Nat. Rev. Drug Discovery 9, 273276 (2010).
http://dx.doi.org/10.1038/nrd3139
54.
54.C. A. Lipinski, “Drug-like properties and the causes of poor solubility and poor permeability,” J. Pharmacol. Toxicol. Methods 44, 235249 (2000).
http://dx.doi.org/10.1016/S1056-8719(00)00107-6
55.
55.B. Ramsundar, S. Kearnes, P. Riley, D. Webster, D. Konerding, and V. Pande, “Massively multitask networks for drug discovery,” e-print arXiv:1502.02072 (2015).
56.
56.I. Buch, T. Giorgino, and G. De Fabritiis, “Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations,” Proc. Natl. Acad. Sci. U. S. A. 108, 1018410189 (2011).
http://dx.doi.org/10.1073/pnas.1103547108
57.
57.D. W. Borhani and D. E. Shaw, “The future of molecular dynamics simulations in drug discovery,” J. Comput.-Aided Mol. Des. 26, 1526 (2012).
http://dx.doi.org/10.1007/s10822-011-9517-y
58.
58.J. Mortier, C. Rakers, M. Bermudez, M. S. Murgueitio, S. Riniker, and G. Wolber, “The impact of molecular dynamics on drug design: Applications for the characterization of ligand–macromolecule complexes,” Drug Discovery Today 20, 686 (2015).
http://dx.doi.org/10.1016/j.drudis.2015.01.003
59.
59.W. Sinko, S. Lindert, and J. A. McCammon, “Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design,” Chem. Biol. Drug Des. 81, 4149 (2013).
http://dx.doi.org/10.1111/cbdd.12051
60.
60.J. Wereszczynski and J. A. McCammon, “Statistical mechanics and molecular dynamics in evaluating thermodynamic properties of biomolecular recognition,” Q. Rev. Biophys. 45, 125 (2012).
http://dx.doi.org/10.1017/S0033583511000096
61.
61.Y. Okamoto, “Generalized-ensemble algorithms: Enhanced sampling techniques for Monte Carlo and molecular dynamics simulations,” J. Mol. Graphics Modell. 22, 425439 (2004).
http://dx.doi.org/10.1016/j.jmgm.2003.12.009
62.
62.D. M. Zuckerman, “Equilibrium sampling in biomolecular simulation,” Ann. Rev. Biophys. 40, 41 (2011).
http://dx.doi.org/10.1146/annurev-biophys-042910-155255
63.
63.A. M. Ferrenberg and R. H. Swendsen, “Optimized Monte Carlo data analysis,” Phys. Rev. Lett. 63, 1195 (1989).
http://dx.doi.org/10.1103/PhysRevLett.63.1195
64.
64.S. Kumar, J. M. Rosenberg, D. Bouzida, R. H. Swendsen, and P. A. Kollman, “Multidimensional free-energy calculations using the weighted histogram analysis method,” J. Comput. Chem. 16, 13391350 (1995).
http://dx.doi.org/10.1002/jcc.540161104
65.
65.M. R. Shirts and J. D. Chodera, “Statistically optimal analysis of samples from multiple equilibrium states,” J. Chem. Phys. 129, 124105 (2008).
http://dx.doi.org/10.1063/1.2978177
66.
66.B. Roux, “The calculation of the potential of mean force using computer simulations,” Comput. Phys. Commun. 91, 275282 (1995).
http://dx.doi.org/10.1016/0010-4655(95)00053-I
67.
67.J. D. Chodera, W. C. Swope, J. W. Pitera, and K. A. Dill, “Long-time protein folding dynamics from short-time molecular dynamics simulations,” Multiscale Model. Simul. 5, 12141226 (2006).
http://dx.doi.org/10.1137/06065146X
68.
68.Y. Deng and B. Roux, “Computations of standard binding free energies with molecular dynamics simulations,” J. Phys. Chem. B 113, 22342246 (2009).
http://dx.doi.org/10.1021/jp807701h
69.
69.P. Nielaba, M. Mareschal, and G. Ciccotti, Bridging the Time Scales: Molecular Simulations for the Next Decade (Springer Science and Business Media, 2002), Vol. 605.
70.
70.G. R. Bowman, V. S. Pande, and F. Noé, An Introduction to Markov State Models and their Application to Long Timescale Molecular Simulation (Springer Science and Business Media, 2013), Vol. 797.
71.
71.J. L. MacCallum, A. Perez, and K. A. Dill, “Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference,” Proc. Natl. Acad. Sci. U. S. A. 112, 6985 (2015).
http://dx.doi.org/10.1073/pnas.1506788112
72.
72.T. Bereau and K. Kremer, “Automated parametrization of the coarse-grained Martini force field for small organic molecules,” J. Chem. Theory Comput. 11, 27832791 (2015).
http://dx.doi.org/10.1021/acs.jctc.5b00056
73.
73.T. Bereau, D. Andrienko, and O. A. von Lilienfeld, “Transferable atomic multipole machine learning models for small organic molecules,” J. Chem. Theory Comput. 11, 32253233 (2015).
http://dx.doi.org/10.1021/acs.jctc.5b00301
74.
74.J. Behler and M. Parrinello, “Generalized neural-network representation of high-dimensional potential-energy surfaces,” Phys. Rev. Lett. 98, 146401 (2007).
http://dx.doi.org/10.1103/PhysRevLett.98.146401
75.
75.J. Behler, “Neural network potential-energy surfaces in chemistry: A tool for large-scale simulations,” Phys. Chem. Chem. Phys. 13, 1793017955 (2011).
http://dx.doi.org/10.1039/c1cp21668f
76.
76.Z. Li, J. R. Kermode, and A. De Vita, “Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces,” Phys. Rev. Lett. 114, 096405 (2015).
http://dx.doi.org/10.1103/PhysRevLett.114.096405
77.
77.P. Gasparotto and M. Ceriotti, “Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond,” J. Chem. Phys. 141, 174110 (2014).
http://dx.doi.org/10.1063/1.4900655
78.
78.Z. D. Pozun, K. Hansen, D. Sheppard, M. Rupp, K.-R. Müller, and G. Henkelman, “Optimizing transition states via kernel-based machine learning,” J. Chem. Phys. 136, 174101 (2012).
http://dx.doi.org/10.1063/1.4707167
79.
79.R. Olivares-Amaya, C. Amador-Bedolla, J. Hachmann, S. Atahan-Evrenk, R. S. Sánchez-Carrera, L. Vogt, and A. Aspuru-Guzik, “Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics,” Energy Environ. Sci. 4, 48494861 (2011).
http://dx.doi.org/10.1039/C1EE02056K
80.
80.V. Rühle, A. Lukyanov, F. May, M. Schrader, T. Vehoff, J. Kirkpatrick, B. Baumeier, and D. Andrienko, “Microscopic simulations of charge transport in disordered organic semiconductors,” J. Chem. Theory Comput. 7, 33353345 (2011).
http://dx.doi.org/10.1021/ct200388s
81.
81.P. Kordt, J. J. M. van der Holst, M. Al Helwi, W. Kowalsky, F. May, A. Badinski, C. Lennartz, and D. Andrienko, “Modeling of organic light emitting diodes: From molecular to device properties,” Adv. Funct. Mater. 25, 19551971 (2015).
http://dx.doi.org/10.1002/adfm.201403004
82.
82.C. Poelking, K. Daoulas, A. Troisi, and D. Andrienko, “Morphology and charge transport in P3HT: A theorist’s perspective,” in P3HT Revisited—From Molecular Scale to Solar Cell Devices, Advances in Polymer Science Vol. 265, edited by S. Ludwigs (Springer, Berlin, Heidelberg, 2014), pp. 139180.
83.
83.C. Poelking and D. Andrienko, “Design rules for organic donor-acceptor heterojunctions: Pathway for charge splitting and detrapping,” J. Am. Chem. Soc. 137, 63206326 (2015).
http://dx.doi.org/10.1021/jacs.5b02130
84.
84.M. C. Scharber, D. Wuhlbacher, M. Koppe, P. Denk, C. Waldauf, A. J. Heeger, and C. L. Brabec, “Design rules for donors in bulk-heterojunction solar cells–Towards 10% energy-conversion efficiency,” Adv. Mater. 18, 789 (2006).
http://dx.doi.org/10.1002/adma.200501717
85.
85.C. Poelking, M. Tietze, C. Elschner, S. Olthof, D. Hertel, B. Baumeier, F. Würthner, K. Meerholz, K. Leo, and D. Andrienko, “Impact of mesoscale order on open-circuit voltage in organic solar cells,” Nat. Mater. 14, 434439 (2014).
http://dx.doi.org/10.1038/nmat4167
86.
86.P. Deglmann, A. Schaefer, and C. Lennartz, “Application of quantum calculations in the chemical industry—An overview,” Int. J. Quantum Chem. 115, 107136 (2015).
http://dx.doi.org/10.1002/qua.24811
87.
87.Y. Unger, T. Strassner, and C. Lennartz, “Prediction of the emission wavelengths of metal-organic triplet emitters by quantum chemical calculations,” J. Organomet. Chem. 748, 6367 (2013).
http://dx.doi.org/10.1016/j.jorganchem.2013.07.011
88.
88.F. May, M. Al-Helwi, B. Baumeier, W. Kowalsky, E. Fuchs, C. Lennartz, and D. Andrienko, “Design rules for charge-transport efficient host materials for phosphorescent organic light-emitting diodes,” J. Am. Chem. Soc. 134, 1381813822 (2012).
http://dx.doi.org/10.1021/ja305310r
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/content/aip/journal/aplmater/4/5/10.1063/1.4943287
2016-03-15
2016-09-27

Abstract

Soft matter embodies a wide range of materials, which all share the common characteristics of weak interaction energies determining their supramolecular structure. This complicates structure-property predictions and hampers the direct application of data-driven approaches to their modeling. We present several aspects in which these methods play a role in designing soft-matter materials: drug design as well as information-driven computer simulations, e.g., histogram reweighting. We also discuss recent examples of rational design of soft-matter materials fostered by physical insight and assisted by data-driven approaches. We foresee the combination of data-driven and physical approaches a promising strategy to move the field forward.

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