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8 - The role of quantum mechanics in structure-based drug design

from PART II - COMPUTATIONAL CHEMISTRY METHODOLOGY

Published online by Cambridge University Press:  06 July 2010

By
Kenneth M. Merz Jr.
Edited by
Kenneth M. Merz, Jr ,
Dagmar Ringe  and
Charles H. Reynolds
Kenneth M. Merz, Jr
Affiliation:
University of Florida
Dagmar Ringe
Affiliation:
Brandeis University, Massachusetts
Charles H. Reynolds
Affiliation:
Johnson & Johnson Pharmaceutical Research & Development
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Summary

INTRODUCTION

The routine use of quantum mechanics (QM) in all phases of in silico drug design is the logical next step in the evolution of this field. The first principles nature of QM allows it to systematically improve the accuracy of the description of the nature of the interactions between molecules. Moreover, the systematic way in which one can approach the use of QM methods to solve chemical and biological problems is quite appealing, but the practical use of many of the appealing features of QM in in silico drug design applications is still to be realized in large part because of computational limitations. In recent years it has become clear that classical potential functions are being pushed to their limits and as many pitfalls of using them are coming to light, one is tempted to explore the use of QM procedures. This is a somewhat naïve view, however, because one of the main observations of a large body of computational work has shown that sampling of relevant conformational states can be as important as providing an accurate representation of an inter-or intramolecular interaction. Hence, even as QM becomes a routine tool used to calculate the energy of individual states of a biological system, one still faces the daunting task of sampling relevant conformational space, which, in our view, will for the near term be largely confined to classical models.

Type
Chapter
Information
Drug Design
Structure- and Ligand-Based Approaches
 , pp. 120 - 136
Publisher: Cambridge University Press
Print publication year: 2010

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References

Cavalli, A.; Carloni, P.; Recanatini, M.Target-related applications of first principles quantum chemical methods in drug design. Chem. Rev. 2006, 106, 3497–3519.Google Scholar
Fedorov, D. G.; Kitaura, K.Extending the power of quantum chemistry to large systems with the fragment molecular orbital method. J. Phys. Chem. A 2007, 111, 6904–6914.Google Scholar
Mulholland, A. J.Modelling enzyme reaction mechanisms, specifity and catalysis. Drug. Discov. Today 2005, 10, 1393–1402.Google Scholar
Friesner, R. A.; Gullar, V.Ab initio quantum chemical and mixed quantum mechanics/molecular mechanics (QM/MM) methods for studying enzymatic catalysis. Ann. Rev. Phys. Chem. 2005, 56, 389–427.Google Scholar
Blundell, T. L.; Jhoti, H.; Abell, C.High-throughput crystallography for lead discovery in drug design. Nat. Rev. Drug Discov. 2002, 1(1), 45–54.Google Scholar
Hartshorn, M. J.; Murray, C. W.; Cleasby, A.; Frederickson, M.; Tickle, I. J.; Jhoti, H.Fragment-based lead discovery using X-ray crystallography. J. Med. Chem. 2005, 48(2), 403–413.Google Scholar
Nienaber, V. L.; Richardson, P. L.; Klighofer, V.; Bouska, J. J.; Giranda, V. L.; Greer, J.Discovering novel ligands for macromolecules using X-ray crystallographic screening. Nat. Biotechnol. 2000, 18(10), 1105–1108.Google Scholar
Jack, A.; Levitt, M.Refinement of large structures by simultaneous minimization of energy and R factor. Acta Crystallogr. A 1978, 34, 931–935.Google Scholar
Kleywegt, G. J.; Jones, T. A.Where freedom is given, liberties are taken. Structure 1995, 3(6), 535–540.Google Scholar
Kleywegt, G. J.; Jones, T. A.Databases in protein crystallography. Acta Crystallogr. D Biol. Crystallogr. 1998, 54, 1119–1131.Google Scholar
Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M.CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 1983, 4, 187–217.Google Scholar
Engh, R. A.; Huber, R.Accurate bond and angle parameters for x-ray protein-structure refinement. Acta Crystallogr. A 1991, 47, 392–400.Google Scholar
Cornell, W. D.; Cieplak, P.; Bayly, C. I.; Gould, I. R.; Merz, K. M.; Ferguson, D. M.; Spellmeyer, D. C.; Fox, T.; Caldwell, J. W.; Kollman, P. A.A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 1995, 117(19), 5179–5197.Google Scholar
Davis, A. M.; Teague, S. J.; Kleywegt, G. J.Application and limitations of X-ray crystallographic data in structure-based ligand and drug design. Angew. Chem. Int. Ed. Engl. 2003, 42(24), 2718–2736.Google Scholar
Yu, N.; Li, X.; Cui, G.; Hayik, S. A.; Merz, K. M. Critical assessment of quantum mechanics based energy restraints in protein crystal structure refinement. Protein Sci. 2006, in press.
Yu, N.; Yennawar, H. P.; Merz, K. M.Refinement of protein crystal structures using energy restraints derived from linear-scaling quantum mechanics. Acta Crystallogr. D Biol. Crystallogr. 2005, 61, 322–332.Google Scholar
Nilsson, K.; Ryde, U.Protonation status of metal-bound ligands can be determined by quantum refinement. J. Inorg. Biochem. 2004, 98(9), 1539–1546.Google Scholar
Ryde, U.; Nilsson, K.Quantum chemistry can locally improve protein crystal structures. J. Am. Chem. Soc. 2003, 125(47), 14232–14233.Google Scholar
Ryde, U.; Nilsson, K.Quantum refinement: a method to determine protonation and oxidation states of metal sites in protein crystal structures. J. Inorg. Biochem. 2003, 96(1), 39–39.Google Scholar
Ryde, U.; Nilsson, K.Quantum refinement: a combination of quantum chemistry and protein crystallography. J. Mol. Struct. 2003, 632, 259–275.Google Scholar
Ryde, U.; Olsen, L.; Nilsson, K.Quantum chemical geometry optimizations in proteins using crystallographic raw data. J. Comput. Chem. 2002, 23(11), 1058–1070.Google Scholar
Yu, N.; Hayik, S. A.; Wang, B.; Liao, N.; Reynolds, C. H.; Merz, K. M.Assigning the protonation states of the key aspartates in beta-secretase using QM/MM x-ray structure refinement. J. Chem. Theor. Comput. 2006, 2, 1057–1069.Google Scholar
Nilsson, K.; Hersleth, H. P.; Rod, T. H.; Andersson, K. K.; Ryde, U.The protonation status of compound II in myoglobin, studied by a combination of experimental data and quantum chemical calculations: quantum refinement. Biophys. J. 2004, 87(5), 3437–3447.Google Scholar
Cui, G.; Xue, L.; Merz, J., K. M. Understanding the substrate selectivity and the product regioselectivity of orf2-catalyzed aromatic prenylations. Biochemistry 2006, submitted.
Kuzuyama, T.; Noel, J. P.; Richard, S. B.Structural basis for the promiscuous biosynthetic prenylation of aromatic natural products. Nature 2005, 435(7044), 983–987.Google Scholar
Schiffer, C.; Hermans, J.Promise of advances in simulation methods for protein crystallography: implicit solvent models, time-averaging refinement, and quantum mechanical modeling. Methods Enzymol, 2003, 374, 412–461.Google Scholar
Shuker, S. B.; Hajduk, P. J.; Meadows, R. P.; Fesik, S. W.Discovering high-affinity ligands for proteins: SAR by NMR. Science 1996, 274(5292), 1531–1534.Google Scholar
Homans, S. W.NMR spectroscopy tools for structure-aided drug design. Angew. Chem. Int. Ed. Engl. 2004, 43(3), 290–300.Google Scholar
Lepre, C. A.; Moore, J. M.; Peng, J. W.Theory and applications of NMR-based screening in pharmaceutical research. Chem. Rev. 2004, 104(8), 3641–3676.Google Scholar
Meyer, B., Peters, T.NMR spectroscopy techniques for screening and identifying ligand binding to protein receptors. Angew. Chem. Int. Ed. Engl. 2003, 42(8), 864–890.Google Scholar
Hajduk, P. J.; Huth, J. R.; Fesik, S. W.Druggability indices for protein targets derived from NMR-based screening data. J. Med. Chem. 2005, 48(7), 2518–2525.Google Scholar
Hajduk, P. J.; Huth, J. R.; Tse, C.Predicting protein druggability. Drug Discov. Today 2005, 10(23–24), 1675–1682.Google Scholar
Sitkoff, D.; Case, D. A.Density functional calculations of proton chemical shifts in model peptides. J. Am. Chem. Soc. 1997, 119(50), 12262–12273.Google Scholar
Wishart, D. S.; Watson, M. S.; Boyko, R. F.; Sykes, B. D.Automated 1H and 13C chemical shift prediction using the BioMagResBank. J. Biomol. NMR 1997, 10(4), 329–336.Google Scholar
Iwadate, M.; Asakura, T.; Williamson, M. P.C-alpha and C-beta carbon-13 chemical shifts in proteins from an empirical database. J. Biomol. NMR 1999, 13(3), 199–211.Google Scholar
Xu, X. P.; Case, D. A.Automated prediction of 15N, 13Calpha, 13Cbeta and 13C' chemical shifts in proteins using a density functional database. J. Biomol. NMR 2001, 21(4), 321–333.Google Scholar
McCoy, M. A.; Wyss, D. F., Spatial localization of ligand binding sites from electron current density surfaces calculated from NMR chemical shift perturbations. J. Am. Chem. Soc. 2002, 124(39), 11758–11763.Google Scholar
Wang, B.; Brothers, E. N.; Vaart, A.; Merz, K. M.Fast semiempirical calculations for nuclear magnetic resonance chemical shifts: a divide-and-conquer approach. J. Chem. Phys. 2004, 120(24), 11392–11400.Google Scholar
Wang, B.; Raha, K.; Merz, K. M.Pose scoring by NMR. J. Am. Chem. Soc. 2004, 126(37), 11430–11431.Google Scholar
Abagyan, R.; Totrov, M.High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 2001, 5(4), 375–382.Google Scholar
Wang, B.; Westerhoff, L. M.; Merz, K. M.A critical assessment of the performance of protein−ligand scoring functions based on NMR chemical shift perturbations. J. Med. Chem. 2007, 50(21), 5128–5134.Google Scholar
Cui, G.; Wang, B.; Merz, K. M.Computational studies of the farnesyltransferase ternary complex part I: substrate binding. Biochemistry 2005, 44(50), 16513–16523.Google Scholar
Chou, J. J.; Case, D. A.; Bax, A.Insights into the mobility of methyl-bearing side chains in proteins from (3)J(CC) and (3)J(CN) couplings. J. Am. Chem. Soc. 2003, 125(29), 8959–8966.Google Scholar
Salvador, P.; Dannenberg, J. J.Dependence upon basis sets of trans hydrogen-bond c08-88723-N-15 3-bond and other scalar J-couplings in amide dimers used as peptide models: a density functional theory study. J. Phys. Chem. B 2004, 108(39), 15370–15375.Google Scholar
Fersht, A. R.; Daggett, V.Protein folding and unfolding at atomic resolution. Cell 2002, 108, 1–20.Google Scholar
Baldwin, R. L.In search of the energetic role of peptide hydrogen bonds. J. Biol. Chem. 2003, 278(20), 17581–17588.Google Scholar
Dill, K. A.; Ozkan, S. B.; Shell, M. S.; Weikl, T. R.The protein folding problem. Annu. Rev. Biophys. 2008, 37, 289–316.Google Scholar
Dewar, M. J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P.AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 1985, 107, 3902–3909.Google Scholar
Stewart, J. J. P.Optimization of parameters for semiempirical methods I. Method. J. Comp. Chem. 1989, 10(2), 209–220.Google Scholar
Möhle, K.; Hofmann, H. J.; Thiel, W.Description of peptide and protein secondary structures employing semiempirical methods. J. Comput. Chem. 2001, 22, 509–520.Google Scholar
Wollacott, A. M.; Merz, K. M.Development of a parameterized force field to reproduce semiempirical geometries. J. Chem. Theory Comput. 2006, 2, 1070–1077.Google Scholar
Hendlich, M.; Lackner, P.; Weitckus, S.; Floeckner, H.; Froschauer, R.; Gottsbacher, K.; Casari, G.; Sippl, M. J.Identification of native protein folds amongst a large number of incorrect models: the calculation of low energy conformations from potentials of mean force. J. Mol. Biol. 1990, 216(1), 167–180.Google Scholar
Lazaridis, T.; Karplus, M.Effective energy functions for protein structure prediction. Curr. Opin. Struct. Biol. 2000, 10(2), 139–145.Google Scholar
Park, B.; Levitt, M.Energy functions that discriminate X-ray and near native folds from well-constructed decoys. J. Mol. Biol. 1996, 258(2), 367–392.Google Scholar
Simons, K. T.; Kooperberg, C.; Huang, E.; Baker, D.Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 1997, 268(1), 209–225.Google Scholar
Lee, M. R..; Kollman, P. A.Free-energy calculations highlight differences in accuracy between X-ray and NMR structures and add value to protein structure prediction. Structure 2001, 9(10), 905–916.Google Scholar
Morozov, A. V.; Tsemekhman, K.; Baker, D.Electron density redistribution accounts for half the cooperativity of alpha helix formation. J. Phys. Chem. B 2006, 110(10), 4503–4505.Google Scholar
Khandogin, J.; York, D. M.Quantum descriptors for biological macromolecules from linear-scaling electronic structure methods. Proteins 2004, 56, 724–737.Google Scholar
Khandogin, J.; Musier-Forsyth, K.; York, D. M.Insights into the regioselectivity and RNA-binding affinity of HIV-1 nucleocapsid protein from linear-scaling quantum methods. J. Mol. Biol, 2003, 330, 993–1004.Google Scholar
Dixon, S. L.; Merz, K. M.Semiempirical molecular orbital calculations with linear system size scaling. J. Chem. Phys. 1996, 104(17), 6643–6649.Google Scholar
Dixon, S. L.; Merz, K. M.Fast, accurate semiempirical molecular orbital calculations for macromolecules. J. Chem. Phys. 1997, 107(3), 879–893.Google Scholar
Rajamani, R.; Reynolds, C. H.Modeling the protonation states of catalytic aspartates in b-secretase. J. Med. Chem. 2004, 47, 5159–5166.Google Scholar
Raha, K.; Merz, K. M.Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J. Med. Chem. 2005, 48, 4558–4575.Google Scholar
Hensen, C.; Hermann, J. C.; Nam, K.; Ma, S.; Gao, J.; Holtje, H.A combined QM/MM approach to protein-ligand interaction: polarization effects of HIV-1 protease on selected high affinity inhibitors. J. Med. Chem. 2004, 47, 6673–6680.Google Scholar
Garcia-Viloca, M.; Truhlar, D. G.; Gao, J.Importance of substrate and cofactor polarization in the active site of dihydrofolate reductase. J. Mol. Biol. 2003, 372(2), 549–560.Google Scholar
Claeyssens, F.; Ranaghan, K. E.; Manby, F. R.; Harvey, J. N.; Mulholland, A. J.Multiple high-level QM/MM reaction paths demonstrate transition-state stabilization in chorismate mutase: correlation of barrier height with transition-state stabilization. Chem. Commun. (Camb.) 2005, 40, 5068–5070.Google Scholar
Xu, D.; Zhou, Y.; Xie, D.; Guo, H.Antibiotic binding to monozinc CphA beta-lactamase from Aeromonas hydropila: quantum mechanical/molecular mechanical and density functional theory studies. J. Med. Chem. 2005, 48(21), 6679–6689.Google Scholar
Zhang, X.; Bruice, T. C.A definitive mechanism for chorismate mutase. Biochemistry 2005, 44(31), 10443–10448.Google Scholar
Park, H.; Brothers, E. N.; Merz, K. M.Hybrid QM/MM and DFT investigations of the catalytic mechanism and inhibition of the dinuclear zinc metallo-beta-lactamase CcrA from Bacteroides fragilis. J. Am. Chem. Soc. 2005, 127(12), 4232–4241.Google Scholar
Szefczyk, B.; Mulholland, A. J.; Ranaghan, K. E.; Sokalski, W. A.Differential transition-state stabilization in enzyme catalysis: quantum chemical analysis of interactions in the chorismate mutase reaction and prediction of the optimal catalytic field. J. Am. Chem. Soc. 2004, 126(49), 16148–16159.Google Scholar
Hermann, J. C.; Hensen, C.; Ridder, L.; Mulholland, A. J.; Holtje, H. D.Mechanisms of antibiotic resistance: QM/MM modeling of the acylation reaction of a class A beta-lactamase with benzylpenicillin. J. Am. Chem. Soc. 2005, 127(12), 4454–4465.Google Scholar
Diaz, N.; Suarez, D.; Merz, K. M.; Sordo, T. L.Molecular dynamics simulations of the TEM-1 beta-lactamase complexed with cephalothin. J. Med. Chem. 2005, 48(3), 780–791.Google Scholar
Taylor, R.; Jewsbury, P. J.; Essex, J. W.A review of protein-small molecule docking methods. J. Comput. Aided. Mol. Des. 2002, 16, 151–166.Google Scholar
Schneidman-Duhovny, D.; Nussinov, R.; Wolfson, H. J.Predicting molecular interactions in silico. II. Protein-protein and protein-drug docking. Curr. Med. Chem. 2004, 11, 91–107.Google Scholar
Raha, K.; Merz, K. M.Calculating binding free energy in protein-ligand interaction. Ann. Rep. Comput. Chem. 2005, 1, 113–130.Google Scholar
Irwin, J. J.; Shoichet, B. K.ZINC: a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45(1), 177–182.Google Scholar
Irwin, J. J.; Raushel, F. M.; Shoichet, B. K.Virtual screening against metalloenzymes for inhibitors and substrates. Biochemistry 2005, 44(37), 12316–12328.Google Scholar
Cho, A. E.; Guallar, V.; Berne, B. J.; Friesner, R. A.Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical approach. J. Comp. Chem. 2005, 29, 917–930.Google Scholar
Mlinsek, G.; Novic, M.; Hodoscek, M.; Solmajer, T.Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures. J. Chem. Inf. Comput. Sci. 2001, 41(5), 1286–1294.Google Scholar
Khandelwal, A.; Lukacova, V.; Comez, D.; Kroll, D. M.; Raha, S.; Balaz, S.A combination of docking, QM/MM methods, and MD simulation for binding affinity estimation of metalloprotein ligands. J. Med. Chem. 2005, 48(17), 5437–5447.Google Scholar
Klahn, M.; Braun-Sand, S.; Rosta, E.; Warshel, A.On possible pitfalls of ab initio quantum mechanics/molecular mechanics minimization approaches for studies of enzymatic reactions. J. Phys. Chem. B 2005, 109, 15645–15650.Google Scholar
Raha, K.; Merz, K. M.A quantum mechanics based scoring function: study of zinc-ion mediated ligand binding. J. Am. Chem. Soc. 2004, 126, 1020–1021.Google Scholar
Gogonea, V.; Merz, K. M.Fully quantum mechanical description of proteins in solution. combining linear scaling quantum mechanical methodologies with the Poisson-Boltzmann equation. J. Phys. Chem. A 1999, 103, 5171–5188.Google Scholar
Nikitina, E.; Sulimov, D.; Zayets, V.; Zaitseva, N.Semiempirical calculations of binding enthalpy for protein-ligand complexes. Int. J. Quantum Chem. 2004, 97(2), 747–763.Google Scholar
Vasilyev, V.; Bliznyuk, A. A.Application of semiempirical quantum chemical methods as a scoring function in docking. Theor. Chem. Acc. 2004, 112, 313–317.Google Scholar
Ohno, K.; Mitsuthoshi, W.; Saito, S.; Inoue, Y.; Sakurai, M.Quantum chemical study of the affinity maturation of 48g7 antibody. Theor. Chem. Acc. 2005, 722, 203–211.Google Scholar
Gao, A. M.; Zhang, D. W.; Zhang, J. Z. H.; Zhang, Y. K.An efficient linear scaling method for ab initio calculation of electron density of proteins. Chem. Phys. Lett. 2004, 394(4–6), 293–297.Google Scholar
Chen, X. H.; Zhang, J. Z. H.Theoretical method for full ab initio calculation of DNA/RNA-ligand interaction energy. J. Chem. Phys. 2004, 120, 11386–11391.Google Scholar
Fukuzawa, K.; Kitaura, K.; Uebayasi, M.; Nakata, K.; Kaminuma, T.; Nakano, T.Ab initio quantum mechanical study of the binding energies of human estrogen receptor alpha with its ligands: an application of fragment molecular orbital method. J. Comput. Chem. 2005, 26(1), 1–10.Google Scholar
He, X.; Mei, Y.; Xiang, Y.; Zhang, D. W.; Zhang, J. Z. H.Quantum computational analysis for drug resistance of HIV-1 reverse transcriptase to nevirapine through point mutations. Proteins 2005, 61(2), 423–432.Google Scholar
Raha, K.; Vaart, A. J.; Riley, K. E.; Peters, M. B.; Westerhoff, L. M.; Kim, H.; Merz, K. M.Pairwise decomposition of residue interaction energies using semiempirical quantum mechanical methods in studies of protein-ligand interaction. J. Am. Chem. Soc. 2005, 127(18), 6583–6594.Google Scholar
Ortiz, A. R.; Pisabarro, M. T.; Gago, F.; Wade, R. C.Prediction of drug-binding affinities by comparative binding-energy analysis. J. Med. Chem. 1995, 38(14), 2681–2691.Google Scholar
Peters, M. B.; Merz, K. M.Semiempirical comparative binding energy analysis (SE-COMBINE) of a series of trypsin inhibitors. J. Chem. Theor. Comput. 2006, 2(2), 383–399.Google Scholar
Karelson, M.; Lobanov, V. S.; Katritzky, A. R.Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev. 1996, 96(3), 1027–1044.Google Scholar
Brüstle, M.; Beck, B.; Schindler, T.; King, W.; Mitchell, T.; Clark, T.Descriptors, physical properties, and drug-likeness. J. Med. Chem. 2002, 45, 3345–3355.Google Scholar
Wan, J.; Zhang, L.; Yang, G.; Zhan, C.Quantitative structure–activity relationship for cyclic imide derivatives of protoporphyrinogen oxidase inhibitors: a study of quantum chemical descriptors from density functional theory. J. Chem. Inf. Comput. Sci. 2004, 44, 2099–2105.Google Scholar
Cramer III, R. D.; Patterson, D. E.; Bunce, J. D.Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 1988, 110, 5959–5967.Google Scholar
Klebe, G.Comparative molecular similarity indices: CoMSIA. In: 3D QSAR in Drug Design, Vol. 3, Kubinyi, H.; Folkers, G.; Martin, Y. C.; Eds. London: Kluwer Academic; 1998, 87.
Sutherland, J. J.; O'Brien, L. A.; Weaver, D. F.A comparison of methods for modeling quantitative structure-activity relationships. J. Med. Chem. 2004, 47, 5541–5554.Google Scholar
Dixon, S.; Merz, K. M.; Lauri, G.; Ianni, J. C.QMQSAR: utilization of a semiempirical probe potential in a field-based qsar method. J. Comput. Chem. 2005, 26, 23–34.Google Scholar
Turner, D. B.; Willett, P.; Ferguson, A. M.; Heritage, T.Evaluation of a novel infrared range vibration-based descriptor (EVA) for QSAR studies. 1. General application. J. Comput. Aided Mol. Des. 1997, 11(4), 409–422.Google Scholar
Tuppurainen, K.EEVA (electronic eigenvalue): A new QSAR/QSPR descriptor for electronic substituent effects based on molecular orbital energies. Sar and Qsar in Environ. Res. 1999, 10(1), 39–46.Google Scholar
Bursi, R.; Dao, T.; Wijk, T.; Gooyer, M.; Kellenbach, E.; Verwer, P.Comparative spectra analysis (CoSA): spectra as three-dimensional molecular descriptors for the prediction of biological activities. J. Chem. Inf. Comput. Sci. 1999, 39(5), 861–867.Google Scholar
Asikainen, A.; Ruuskanen, J.; Tuppurainen, K.Spectroscopic QSAR methods and self-organizing molecular field analysis for relating molecular structure and estrogenic activity. J. Chem. Inf. Comput. Sci. 2003, 43(6), 1974–1981.Google Scholar
Besalu, E.; Girones, X.; Amat, L.; Carbo-Dorca, R.Molecular quantum similarity and the fundamentals of QSAR. Acc. Chem. Res. 2002, 35, 289–295.Google Scholar
Carbó-Dorca, R.; Gironés, X.Foundation of quantum similarity measures and their relationship to QSPR: density function structure, approximations, and application examples. Int. J. Quantum Chem. 2005, 101, 8–20.Google Scholar
Bultinck, P.; Kuppens, T.; Gironés, X.; Carbó-Dorca, R.Quantum similarity superposition algorithm (QSSA): a consistent scheme for molecular alignment and molecular similarity based on quantum chemistry. J. Chem. Inf. Comput. Sci. 2003, 43, 1143–1150.Google Scholar
Fusti-Molnar, L.; Merz, K. M.An efficient and accurate molecular alignment and docking technique using ab initio quality scoring. J. Chem. Phys. 2008, 129, 25102–25113.Google Scholar
O'Brien, S. E.; Popelier, P. L. A.Quantum molecular similarity. 3. QTMS descriptors. J. Chem. Inf. Comput. Sci. 2001, 41, 764–775.Google Scholar
Chaudry, U. A.; Popelier, P. L. A.Estimation of pKa using quantum topological molecular similarity descriptors: application to carboxylic acids, anilines and phenols. J. Org. Chem. 2004, 69, 233–241.Google Scholar

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