Tensor algebra-based geometric methodology to codify central chirality on organic molecules

References

• M.R. Islam, J.G. Mahdi, and I.D. Bowen, Pharmacological importance of stereochemical resolution of enantiomeric drugs, Drug Saf. 17 (2012), pp. 149–165.
   Web of Science ®Google Scholar
• K. Williams and E. Lee, Importance of drug enantiomers in clinical pharmacology, Drugs 30 (2012), pp. 333–354.
   Web of Science ®Google Scholar
• A. Golbraikh, D. Bonchev, and A. Tropsha, Novel chirality descriptors derived from molecular topology, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 147–158.
   PubMedGoogle Scholar
• H. Schumacher, D.A. Blake, J.M. Gurian, and J.R. Gillette, A comparison of the teratogenic activity of thalidomide in rabbits and rats, J. Pharmacol. Exp. Ther. 160 (1968), pp. 189–200.
   PubMed Web of Science ®Google Scholar
• S.C. Stinson, Chiral drugs, Chem. Eng. News 78 (2000), pp. 55–78.
   Web of Science ®Google Scholar
• G.M. Crippen, Chirality descriptors in QSAR, Curr. Comput. Aided Drug Des. 4 (2008), pp. 259–264.
   Web of Science ®Google Scholar
• J.V. de Julian-Ortiz, C. de Gregorio Alapont, I. Rıos-Santamarina, R. Garcıa-Domenech, and J. Galvez, Prediction of properties of chiral compounds by molecular topology, J. Mol. Graph. Model. 16 (1998), pp. 14–18.
   PubMed Web of Science ®Google Scholar
• R.M. Stess, R. Mirmiran, J.R. Armstrong, S.R. Jensen, J.V. de Julian-Ortiz, C. de Gregorio Alapont, I. Ros-Santamarina, R. Garca-Domenech, and J. Galvez, Prediction of properties of chiral compounds by molecular topology – A tool for de novo drug design, J. Mol. Graph. Model. 16 (1998), pp. 14–18.
   PubMed Web of Science ®Google Scholar
• H.G. Dı́az, I.H. Sánchez, E. Uriarte, and L. Santana, Symmetry considerations in Markovian chemicals ‘in silico’ design (MARCH-INSIDE) I: Central chirality codification, classification of ACE inhibitors and prediction of σ-receptor antagonist activities, Comput. Biol. Chem. 27 (2003), pp. 217–227.
   PubMed Web of Science ®Google Scholar
• Y. Marrero-Ponce and J.A. Castillo-Garit, 3D-chiral atom, atom-type, and total non-stochastic and stochastic molecular linear indices and their applications to central chirality codification, J. Comput. Aided Mol. Des. 19 (2005), pp. 369–383.
   PubMed Web of Science ®Google Scholar
• Y. Marrero-Ponce, J.A. Castillo-Garit, E.A. Castro, F. Torrens, and R. Rotondo, 3D-chiral (2.5) atom-based TOMOCOMD-CARDD descriptors: Theory and QSAR applications to central chirality codification, J. Math. Chem. 44 (2008), pp. 755–786.
   Web of Science ®Google Scholar
• Y. Marrero Ponce, H.G. Dı́az, V.R. Zaldivar, F. Torrens, and E.A. Castro, 3D-chiral quadratic indices of the ‘molecular pseudograph’s atom adjacency matrix’ and their application to central chirality codification: Classification of ACE inhibitors and prediction of σ-receptor antagonist activities, Bioorg. Med. Chem. 12 (2004), pp. 5331–5342.
   PubMed Web of Science ®Google Scholar
• J.V. de Julián-Ortiz, R. García-Domenech, J. Gálvez Alvarez, R. Soler Roca, F.J. García-March, and G.M. Antón-Fos, Use of topological descriptors in chromatographic chiral separations, J. Chromatogr. A 719 (1996), pp. 37–44.
   Web of Science ®Google Scholar
• S. Fujita, Stereoisograms for reorganizing the theoretical foundations of stereochemistry and stereoisomerism. Part 2: rational avoidance of misleading standpoints for R/S-stereodescriptors of the Cahn–Ingold–Prelog system, Tetrahedron: Asymmetry 25 (2014), pp. 1169–1189.
   Web of Science ®Google Scholar
• G. Helmchen, The 50th Anniversary of the Cahn–Ingold–Prelog Specification of Molecular Chirality, Angewandte Chemie International Edition 55 (2016), pp. 6798–6799.
   PubMed Web of Science ®Google Scholar
• H.P. Schultz, E.B. Schultz, and T.P. Schultz, Topological organic chemistry. 9. Graph theory and molecular topological indices of stereoisomeric organic compounds, J. Chem. Inf. Comput. Sci. 35 (1995), pp. 864–870.
   Google Scholar
• D.H. Wolpert and W.G. Macready, No free lunch theorems for optimization, Evol. Comput. 1 (1997), pp. 67–82.
   Google Scholar
• N. Cubillán, Y. Marrero-Ponce, H. Ariza-Rico, S. Barigye, C. García-Jacas, J. Valdes-Martini, and Y. Alvarado, Novel global and local 3D atom-based linear descriptors of the Minkowski distance matrix: Theory, diversity–variability analysis and QSPR applications, J. Math. Chem. 53 (2015), pp. 2028–2064.
   Web of Science ®Google Scholar
• Y. Marrero-Ponce, C.R. García-Jacas, S.J. Barigye, J.R. Valdés-Martiní, O.M. Rivera-Borroto, R.W. Pino-Urias, N. Cubillán, Y.J. Alvarado, and H. Le-Thi-Thu, Optimum search strategies or novel 3D molecular descriptors: Is there a stalemate?, Curr. Bioinform. 10 (2015), pp. 533–564.
   Web of Science ®Google Scholar
• C.R. García-Jacas, Y. Marrero-Ponce, S.J. Barigye, J.R. Valdés-Martiní, O.M. Rivera-Borroto, and J. Olivero-Verbel, N-linear algebraic maps for chemical structure codification: A suitable generalization for atom-pair approaches? Curr. Drug Metb. 15 (2014), pp. 441–469.
   PubMed Web of Science ®Google Scholar
• C.R. García-Jacas, Y. Marrero-Ponce, S.J. Barigye, T. Hernández-Ortega, L. Cabrera-Leyva, and A. Fernández-Castillo, N-tuple topological/geometric cutoffs for 3D N-linear algebraic molecular codifications: Variability, linear independence and QSAR analysis, SAR QSAR Environ. Res. 27 (2016), pp. 949–975.
   PubMed Web of Science ®Google Scholar
• J.W. Godden, F.L. Stahura, and J. Bajorath, Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations, J. Chem. Inf. Model. 40 (2000), pp. 796–800.
   Web of Science ®Google Scholar
• R.W.P. Urias, S.J. Barigye, Y. Marrero-Ponce, C.R. García-Jacas, J.R. Valdes-Martiní, and F. Perez-Gimenez, IMMAN: Free software for information theory-based chemometric analysis, Mol. Divers. 19 (2015), pp. 305–319.
   PubMed Web of Science ®Google Scholar
• C.R. García-Jacas, E. Contreras-Torres, Y. Marrero-Ponce, M. Pupo-Meriño, S.J. Barigye, and L. Cabrera-Leyva, Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets, J. Cheminform. 8 (2016), pp. 1–16.
   PubMed Web of Science ®Google Scholar
• C.R. García-Jacas, K. Martinez-Mayorga, Y. Marrero-Ponce, and J. Medina-Franco, Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators, SAR QSAR Environ. Res. 28 (2017), pp. 41–58.
   PubMed Web of Science ®Google Scholar
• C.R. García-Jacas, L. Aguilera-Mendoza, R. González-Pérez, Y. Marrero-Ponce, L. Acevedo-Martínez, S.J. Barigye, and T. Avdeenko, Multi-server approach for high-throughput molecular descriptors calculation based on multi-linear algebraic maps, Mol. Inform. 34 (2015), pp. 60–69.
   PubMed Web of Science ®Google Scholar
• C.R. García-Jacas, Y. Marrero-Ponce, L. Acevedo-Martínez, S.J. Barigye, J.R. Valdés-Martiní, and E. Contreras-Torres, QuBiLS-MIDAS: A parallel free-software for molecular descriptors computation based on multilinear algebraic maps, J. Comput. Chem. 35 (2014), pp. 1395–1409.
   PubMed Web of Science ®Google Scholar
• E.L. Eliel, S.H. Wilen, and L.N. Mandel, Stereochemistry of organic compounds, Rec. Trav. Chim. Pays Bas 114 (1995), p. 378.
   Google Scholar
• R.D. Cramer, D.E. Patterson, and J.D. Bunce, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc. 110 (1988), pp. 5959–5967.
   PubMed Web of Science ®Google Scholar
• D. Robert, L. Amat, and R. Carbó-Dorca, Three-dimensional quantitative–activity relationships from tuned molecular quantum similarity measures: Prediction of the corticosteroid-binding globulin binding affinity for a steroid family, J. Chem. Inf. Comput. Sci. 39 (1999), pp. 333–344.
   PubMedGoogle Scholar
• N. Stiefl and K. Baumann, Mapping property distributions of molecular surfaces: Algorithm and evaluation of a novel 3D quantitative structure−activity relationship technique, J. Med. Chem. 46 (2003), pp. 1390–1407.
   PubMed Web of Science ®Google Scholar
• M. Pastor, G. Cruciani, I. McLay, P. Pickett, and S. Clementi, GRid-INdependent descriptors (GRIND): A novel class of alignment-independent three-dimensional molecular descriptors, J. Med. Chem. 43 (2000), pp. 3233–3243.
   PubMed Web of Science ®Google Scholar
• A.N. Jain., K. Koile., and D. Chapman, Compass: Predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark, J. Med. Chem. 37 (1994), pp. 2315–2327.
   PubMed Web of Science ®Google Scholar
• R. Todeschini, V. Consonni, A. Mauri, M. Pavan, and R. Leardi, MobyDigs: Software for regression and classification models by genetic algorithms, in Data Handling in Science and Technology, R. Leardi, ed., Elsevier, Amsterdam, The Netherlands, 2003, pp. 141–167.
   Google Scholar
• M. Friedman, A Comparison of alternative tests of significance for the problem of $m$ rankings, Ann. Math. Statist. 11 (1940), pp. 86–92.
   Google Scholar
• J. Alcalá-Fdez, L. Sánchez, S. García, M.J.d. Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, and F. Herrera, KEEL: A software tool to assess evolutionary algorithms for data mining problems, Soft Comput. 13 (2009), pp. 307–318.
   Web of Science ®Google Scholar
• J.A.C. Garit, R.G. Domenech, Y.M. Ponce, F.T. Zaragozá, and C.A. Mazarío, Prediction of the binding affinity between fenoterol derivatives and the b2 adrenergic receptor using atom-based 3D-chiral linearindices, Nereis: Revista iberoamericana interdisciplinar de métodos, modelización y simulación (2012), pp. 9–18.
   Google Scholar
• J. Castillo-Garit, R. García-Domenech, Y. Marrero-Ponce, F. Torrens, and C. Abad, Atom-based 3D-chiral quadratic indices. Part 3: Prediction of the binding affinity of the stereoisomers of fenoterolto the β2 adrenergic receptor, Afinidad 68 (2011).
   Web of Science ®Google Scholar
• K. Jozwiak, C. Khalid, M.J. Tanga, I. Berzetei-Gurske, L. Jimenez, J.A. Kozocas, A. Woo, W. Zhu, R.-P. Xiao, D.R. Abernethy, and I.W. Wainer, Comparative molecular field analysis of the binding of the stereoisomers of fenoterol and fenoterol derivatives to the β2 adrenergic receptor, J. Med. Chem. 50 (2007), pp. 2903–2915.
   PubMed Web of Science ®Google Scholar
• H. Van de Waterbeemd, N. El Tayar, B. Testa, H. Wikstroem, and B. Largent, Quantitative structure–activity relationships and eudismic analyses of the presynaptic dopaminergic activity and dopamine D2 and.sigma. receptor affinities of 3-(3-hydroxyphenyl)piperidines and octahydrobenzo[f]quinolines, J. Med. Chem. 30 (1987), pp. 2175–2181.
   PubMed Web of Science ®Google Scholar
• C. Yang and C. Zhong, Chirality factors and their application to QSAR studies of chiral molecules, QSAR Comb. Sci. 24 (2005), pp. 1047–1055.
   Google Scholar
• S.R. O'Donnell, A selective β-adrenoreceptor stimulant (Th1165a) related to orciprenaline, Eur. J. Pharmacol. 12 (1970), pp. 35–43.
   PubMed Web of Science ®Google Scholar
• R.C. Heel, R.N. Brogden, T.M. Speight, and G.S. Avery, Fenoterol: A review of its pharmacological properties and therapeutic efficacy in asthma, Drugs 15 (1978), pp. 3–32.
   PubMed Web of Science ®Google Scholar
• R.-P. Xiao, S.-J. Zhang, K. Chakir, P. Avdonin, W. Zhu, R.A. Bond, C.W. Balke, E.G. Lakatta, and H. Cheng, Enhanced Gi signaling selectively negates β2-adrenergic receptor (AR)– but not β1-ar–mediated positive inotropic effect in myocytes from failing rat hearts, Circulation 108 (2003), pp. 1633–1639.
   PubMed Web of Science ®Google Scholar
• B.L. Largent, A.L. Gundlach, and S.H. Snyder, Psychotomimetic opiate receptors labeled and visualized with (+)-[3H]3-(3-hydroxyphenyl)-N-(1-propyl)piperidine, Proc. Natl. Acad. Sci. USA 81 (1984), pp. 4983–4987.
   PubMed Web of Science ®Google Scholar
• M. Vincent, B. Marchand, G. Rémond, S. Jaguelin-Guinamant, G. Damien, B. Portevin, J.Y. Baumal, J.P. Volland, J.P. Bouchet, and P.H. Lambert, Synthesis and ACE inhibitory activity of the stereoisomers of perindopril (S 9490) and perindoprilate (S 9780), Drug Des. Discov. 9 (1992), pp. 11–28.
   PubMedGoogle Scholar
• A.K. Rappe, C.J. Casewit, K.S. Colwell, W.A. Goddard, and W.M. Skiff, UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations, J. Am. Chem. Soc. 114 (1992), pp. 10024–10035.
   Web of Science ®Google Scholar
• J.-P. Ebejer, G.M. Morris, and C.M. Deane, Freely available conformer generation methods: How good are they? J. Chem. Inf. Model. 52 (2012), pp. 1146–1158.
   PubMed Web of Science ®Google Scholar
• J.A. Castillo-Garit, Y. Marrero-Ponce, F. Torrens, R. García-Domenech, and J.E. Rodríguez-Borges, Applications of bond-based 3D-chiral quadratic indices in QSAR studies related to central chirality codification, QSAR Comb. Sci. 28 (2009), pp. 1465–1477.
   Google Scholar
• J.A. Castillo-Garit, Y. Marrero-Ponce, F. Torrens, R. Garcia-Domenech, and V. Romero-Zaldivar, Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification, J. Comput. Chem. 29 (2008), pp. 2500–2512.
   PubMed Web of Science ®Google Scholar
• J.A. Castillo-Garit, Y. Marrero-Ponce, F. Torrens, and R. Rotondo, Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification, J. Mol. Graph. Model. 26 (2007), pp. 32–47.
   PubMed Web of Science ®Google Scholar