Autobiography of Roberto Todeschini

Document Type : Research Paper

Author

Milano Chemometrics and QSAR Research Group

Main Subjects

References

  1. Ballabio, D., Biganzoli, F., Todeschini, R. and Consonni, V. (2016) Qualitative consensus of QSAR ready biodegradability predictions. Toxicological and Environmental Chemistry, in press.
  2. Cassotti, M., Grisoni, F., Nembri, S. and Todeschini, R. (2016) Application of the weighted Power-Weakness Ratio (wPWR) as a fusion rule in ligand-based virtual screening. MATCH Commun. Math. Comput. Chem., 76, 359–376.
  3. Grisoni, F., Consonni, V., Vighi, M., Villa, S. and Todeschini, R. (2016) Investigating the mechanisms of bioconcentration through QSAR classification trees. Environmental International, 88, 198–205.
  4. Grisoni, F., Consonni, V., Vighi, M., Villa, S. and Todeschini, R. (2016) Expert QSAR system for predicting the bioconcentration factor under the REACH regulation. EnvironmentalResearch, 148, 507–512.
  5. Grisoni, F., Reker, D., Schneider, P., Friedrich, L., Consonni, V., Todeschini, R., Koberle, A., Werz, O. and Schneider, G. (2016) Matrix-based molecular descriptors for prospective virtual compound screening. MolecularInformatics, 35.
  6. Mauri, A., Consonni, V. and Todeschini, R. (2016) Molecular Descriptors, in Handbook of Computational Chemistry (ed. T. Puzyn), Springer.
  7. Mauri, A., Ballabio, D., Todeschini, R. and Consonni, V. (2016) Mixtures, metabolites, ionic liquids: anew measure to evaluate similarity between complex chemical systems. Journal of Cheminformatics, 8, 1-3.
  8. Nembri, S., Grisoni, F., Consonni, V. and Todeschini, R. (2016) In silico prediction of Cytochrome P450 - Drug interaction: QSARs for CYP3A4 and CYP2C9. International Journal of MolecularSciences, 17, 1–19.
  9. Rojas, Ch., Ballabio, D., Consonni, V., Tripaldi, P., Mauri, A. and Todeschini, R. (2016) Quantitative Structure-Activity Relationships to predict sweet and non-sweet tastes. TheoreticalChemistry Accounts, 135–166.
  10. Todeschini, R., Ballabio, D., Grisoni, F. and Consonni, V. (2016) A new concept of second-order similarity and the role od distance/similarity measures in local classification methods. Chemometrics &Intell. Lab. Syst., 157, 50–57.
  11. Todeschini, R. and Baccini, A. (2016) Handbook of Bibliometric Indicators, Wiley-VCH, Weinheim (Germany), 512 pp.
  12. Todeschini, R., Ballabio, D. and Grisoni, F. (2016) Beware of unreliable Q2! A comparative study of regression metrics for predictivity assessment of QSAR models. Journal of Chemical Information and Modeling, 56, 1905-1913.
  13. Cassotti, M., Ballabio, D., Todeschini, R. and Consonni, V. (2015) A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephalespromelas). SAR & QSAR in EnvironmentalResearch, 26, 217–243.
  14. Grisoni, F., Consonni, V., Nembri, S. and Todeschini, R. (2015) How to weight Hasse matrices and reduce incomparabilities. Chemometrics &Intell. Lab. Syst., 147, 95–104.
  15. Grisoni, F., Consonni, V., Villa, S., Vighi, M. and Todeschini, R. (2015) QSAR models for bioconcentration: is the increase in the complexity justified by more accurate predictions? Chemosphere, 127, 171–179.
  16. Mansouri, K. and et al. (2015) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. EnvironmentalHealthPerspectives, 124, 1023–1033.
  17. Todeschini, R., Ballabio, D. and Consonni, V. (2015) Distances and Other Dissimilarity Measures in Chemometrics, inEncyclopedia of Analytical Chemistry Wiley & Sons, pp. 1–60.
  18. Todeschini, R., Nembri, S. and Grisoni, F. (2015) Weighted Power-Weakness Ratio for multi-criteria decision making. Chemometrics &Intell. Lab. Syst., 146, 329–336.
  19. Todeschini, R., Ballabio, D., Cassotti, M. and Consonni, V. (2015) N3 and BNN: Two new similarity based classification methods in comparison with other classifiers. Journal of Chemical Information and Modeling, 55, 2365–2375.
  20. Ballabio, D., Consonni, V., Mauri, A., Claeys-Bruno, M., Sergent, M. and Todeschini, R. (2014) A novel variable reduction method adapted from space-filling designs. Chemometrics &Intell. Lab. Syst., 136, 147–154.
  21. Buscema, M., Consonni, V., Ballabio, D., Mauri, A., Massini, G., Breda, M. and Todeschini, R. (2014) K-CM: a new artificialneural network. Application to supervised pattern recognition. Chemometrics &Intell. Lab. Syst., 138, 110–119.
  22. Cassotti, M., Ballabio, D., Consonni, V., Mauri, A., Tetko, I.V. and Todeschini, R. (2014) Prediction of acute aquatic toxicity toward daphnia magna using GA-kNN methods. ATLA, 42, 31–41.
  23. Cassotti, M., Grisoni, F. and Todeschini, R. (2014) Reshaped Sequential Replacement algorithm: an efficient approach to variable selection. Chemometrics &Intell. Lab. Syst., 133, 136–148.
  24. Cherkasov, A., Muratov, E., Fourches, D., Varnek, A., Baskin, I., Cronin, M.T.D., Dearden, J., Gramatica, P., Martin, Y.C., Todeschini, R., Consonni, V., Kuz'min, V., Cramer, R., Benigni, R., Yang, C., Richrad, A., Terfloth, L., Gasteiger, J. and Tropsha, A. (2014) QSAR Modeling: Where have you been? Where are yougoing to? Journal MedicinalChemistry, 57, 4997–5010.
  25. Grisoni, F., Cassotti, M. and Todeschini, R. (2014) Reshaped Sequential Replacement algorithm for variable selection in QSPR modelling: comparison with other benchmark methods. Journal of Chemometrics, 28, 249–259.
  26. Sahigara, F., Ballabio, D., Todeschini, R. and Consonni, V. (2014) Assessing the validity of QSARs for ready biodegradability of chemicals: An Applicability Domain perspective. Current Computer-AidedDrug Design, 10, 137–147.
  27. Swapnil, C., Nicholls, I., Karlsson, B., Rosengren, A., Ballabio, D., Consonni, V. and Todeschini, R. (2014) Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study. International Journal of MolecularSciences, 15, 18162–18174.
  28. Tetko, I.V., Schramm, K.-W., Knepper, T., Peijnenburg, W.J.G.M., Hendriks, A.J., Nicholls, I.A., Öberg, T., Todeschini, R., Schlosser, E. and Brandmaier, S. (2014) The Experimental and Theoretical Studies within the FP7 Environmental ChemOinformatics Marie Curie Initial Training Network 'ECO'. ATLA, 42, 1–5.
  29. Todeschini, R., Consonni, V., Ballabio, D., Mauri, A., Cassotti, M., Lee, S., West, A. and Cartlidge, D. (2014) QSPR study of rheological and mechanical properties of Chloroprene rubber accelerators. RubberChemistry and Technology, 87, 219–238.
  30. Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R. and Consonni, V. (2013) Quantitative Structure-Activity Relationship models for ready biodegradability of chemicals. Journal of Chemical Information and Modeling, 53, 867–878.
  31. Sahigara, F., Ballabio, D., Todeschini, R. and Consonni, V. (2013) Defining a novel k-Nearest Neighbours approach to assess the applicability of a QSAR model for reliable predictions. Journal of Chemoinformatics, 5, 1–9.
  32. Todeschini, R., Ballabio, D., Consonni, V., Sahigara, F. and Filzmoser, P. (2013) Locally-centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Anal. Chim. Acta, 787, 1–9.
  33. Consonni, V. and Todeschini, R. (2012) Multivariate Analysis of Molecular Descriptors, in Statistical Modelling of Molecular Descriptors in QSAR/QSPR (eds. M. Dehmer, K. Varmuza and D. Bonchev), Wiley-Blackwell, Weinheim (Germany), pp. 111–147.
  34. Consonni, V. and Todeschini, R. (2012) New similarity coefficients for binary data. MATCH Commun. Math. Comput. Chem., 68, 581–592.
  35. Ippolito, A., Todeschini, R. and Vighi, M. (2012) Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits. Ecotoxicology, 21, 336–352.
  36. Mansouri, K., Consonni, V., Durjava, M.K., Kolar, B., Öberg, T. and Todeschini, R. (2012) Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling. Chemosphere, 89, 433–444.
  37. Nielsen, N.J., Ballabio, D., Tomasi, G., Todeschini, R. and Christensen, J.H. (2012) Chemometric analysis of GC-FID chromatograms (CHEMFID): A novel method for classification of petroleum products. J. Chromat. A, 1238, 121–127.
  38. Sahigara, F., Mansouri, K., Ballabio, D., Mauri, A., Consonni, V. and Todeschini, R. (2012) Comparison of Different Approaches to Define the Applicability Domain of QSAR Models. Molecules, 17, 4791–4810.
  39. Todeschini, R., Consonni, V., Xiang, H., Holliday, J., Buscema, M. and Willett, P. (2012) Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real datasets. Journal of Chemical Information and Modeling, 52, 2884–2901.
  40. Consonni, V. and Todeschini, R. (2011) Structure - Activity Relationships by autocorrelation descriptors and genetic algorithms, inChemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques (eds. H. Lohdi and Y. Yamanishi), IGI Global Publishers, Hershey, PA (USA), pp. 60–93.
  41. Sushko, I., Novotarskyi, S., Körner, R., Pandey, A.K., Rupp, M., Teetz, W., Brandmaier, S., Abdelaziz, A., Prokopenko, V.V., Tanchuk, V.Y., Todeschini, R., Varnek, A., Marcou, G., Ertl, P., Potemkin, V., Grishina, M., Gasteiger, J., Schwab, C., Baskin, I., Palyulin, V.A., Radchenko, E.V., Welsh, W.J., Kholodovych, V., Chekmarev, D., Cherkasov, A., Aires-de-Sousa, J., Zhang, Q.-Y., Bender, A., Nigsch, F., Patiny, L., Williams, A., Tkachenko, V. and Tetko, I.V. (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J. Computer-AidedMol. Des., 25, 533–554.
  42. Todeschini, R. (2011) The j-index: a new bibliometric index and multivariate comparisons between other common indices. Scientometrics, 87, 621–639.
  43. Ballabio, D., Consonni, V., Mauri, A. and Todeschini, R. (2010) Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variableselection in classification. Anal. Chim. Acta, 657, 116–122.
  44. Ballabio, D. and Todeschini, R. (2010) Geographical characterization of olive oil by means of multivariate classification: application of CAIMAN, in Olives and olive oil in health and disease prevention (eds. V. R. Preedy and R. R. Watson), Elsevier, Amsterdam, pp. 131–139.
  45. Consonni, V. and Todeschini, R. (2010) Molecular Descriptors, in Recent Advances in QSAR Studies: Methods and Applications, Vol. 8 (eds. M. T. D. Cronin, J. Leszczynski and T. Puzyn), Springer, Amsterdam (The Netherlands), pp. 29–102.
  46. Consonni, V., Ballabio, D. and Todeschini, R. (2010) Evaluation of model predictive ability by external validation techniques. Journal of Chemometrics, 24, 194–201.
  47. Consonni, V., Ballabio, D. and Todeschini, R. (2010) Enhancing Chemical Information in QSAR: Generalized Graph-Theoretical Matrices, in Novel Molecular Structure Descriptors - Theory and Applications II (eds. I. Gutman and B. Furtula), University of Kragujevac, Kragujevac (Serbia), pp. 21–55.
  48. Fernandez-Varela, R., Gomez-Carracedo, M.P., Ballabio, D., Andrade, J.M., Consonni, V. and Todeschini, R. (2010) Self Organizing Maps For Analysis Of Polycyclic Aromatic Hydrocarbons 3-Way Data From Spilled Oils. AnalyticalChemistry, 82, 4264–4271.
  49. Sushko, I., Novotarskyi, S., Körner, R., Pandey, A.K., Cherkasov, A., Li, J., Gramatica, P., Hansen, K., Schroeter, T., Müller, K.-R., Xi, L., Liu, H., Yao, X., Öberg, T., Hormozdiari, F., Dao, P., Sahinalp, C., Todeschini, R., Polishchuk, P., Artemenko, A., Kuz'min, V., Martin, T.M., Young, D.M., Fourches, D., Muratov, E., Tropsha, A., Baskin, I., Horbath, D., Marcou, G., Varnek, A., Prokopenko, V.V. and Tetko, I.V. (2010) ApplicabilityDomains for ClassificationProblems: Benchmarking of Distance to Models for AmesMutagenicity Set. Journal of Chemical Information and Modeling, 50, 2094–2111.
  50. Todeschini, R. and Consonni, V. (2010) New local vertex invariants and molecular descriptors based on functions of the vertex degrees. MATCH Commun. Math. Comput. Chem., 64, 359–372.
  51. Todeschini, R., Ballabio, D. and Consonni, V. (2010) Novel Molecular Descriptors Based on Functions of New Vertex Degrees, in Novel Molecular Structure Descriptors - Theory and Applications I (eds. I. Gutman and B. Furtula), University of Kragujevac, Kragujevac (Serbia), pp. 73–100.
  52. Ballabio, D. and Todeschini, R. (2009) Multivariate Classification for Qualitative Analysis, in Infrared Spectroscopy for Food Quality Analysis and Control (ed. S. Da-Wen), Elsevier, Amsterdam, pp. 83–104.
  53. Ballabio, D., Manganaro, A., Consonni, V., Mauri, A. and Todeschini, R. (2009) Introduction to MOLE DB – on-line MolecularDescriptors Database. MATCH Commun. Math. Comput. Chem., 62, 199–207.
  54. Ballabio, D., Consonni, V. and Todeschini, R. (2009) The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for Self Organising Maps and Counterpropagation Artificial Neural Networks. Chemometrics &Intell. Lab. Syst., 98, 115–122.
  55. Consonni, V., Ballabio, D., Manganaro, A., Mauri, A. and Todeschini, R. (2009) Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 2. Variablereduction. Anal. Chim. Acta, 648, 52–59.
  56. Consonni, V., Ballabio, D. and Todeschini, R. (2009) Comments on the definition of the Q2 parameter for QSAR validation. Journal of Chemical Information and Modeling, 49, 1669–1678.
  57. Pavan, M. and Todeschini, R. (2009) Multicriteria Decision Making Methods, in Comprehensive Chemometrics, Vol. 1 (eds. B. Walczak, R. Taulér and S. Brown), Elsevier, Amsterdam (The Netherlands), pp. 591–629.
  58. Piazza, L., Gigli, J., Rojas, Ch., Ballabio, D., Todeschini, R. and Tripaldi, P. (2009) Dairy Cream Response In Instrumental Texture Evaluation Processed By Multivariate Analysis. Chemometrics &Intell. Lab. Syst., 96, 258–263.
  59. Todeschini, R., Consonni, V. and Gramatica, P. (2009) Chemometrics in QSAR, in Comprehensive Chemometrics, vol. 4, Vol. 4 (eds. S. Brown, B. Walczak and R. Taulér), Elsevier, Oxford (UK), pp. 129–172.
  60. Todeschini, R. and Consonni, V. (2009) Molecular Descriptors for Chemoinformatics (2 volumes), Vol. 41, WILEY-VCH, Weinheim (Germany), 1257 pp.
  61. Todeschini, R., Consonni, V., Manganaro, A., Ballabio, D. and Mauri, A. (2009) Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications. Anal. Chim. Acta, 648, 45–51.
  62. Consonni, V. and Todeschini, R. (2008) New Spectral Indices for Molecule Description. MATCH Commun. Math. Comput. Chem., 60, 3–14.
  63. Gutman, I., Indulal, G. and Todeschini, R. (2008) Generalizing the McClelland Bounds for Total -Electron Energy. ZeitschriftfürNaturforschung A, 63a, 280–282.
  64. Manganaro, A., Ballabio, D., Consonni, V., Mauri, A., Pavan, M. and Todeschini, R. (2008) The DART (Decision Analysis by Ranking Techniques) software, in Scientific Data Ranking Methods: Theory and Applications (eds. M. Pavan and R. Todeschini), Elsevier, Amsterdam (The Netherlands), pp. 193–207.
  65. Mauri, A., Ballabio, D., Consonni, V., Manganaro, A. and Todeschini, R. (2008) Peptides multivariate characterisation using a molecular descriptor based approach. MATCH Commun. Math. Comput. Chem., 60, 671–690.
  66. Pavan, M. and Todeschini, R. (2008) Total order ranking methods, in Scientific Data Ranking Methods: Theory and Applications(eds. M. Pavan and R. Todeschini), Elsevier, Amsterdam (The Netherlands), pp. 51–72.
  67. Todeschini, R. and Pavan, M., Eds. (2008) Scientific Data Ranking Methods: Theory and Applications.Elsevier, Amsterdam (The Netherlands), 180 pp.
  68. Tetko, I.V., Sushko, I., Pandey, A.K., Zhu, H., Tropsha, A., Papa, E., Õberg, T., Todeschini, R., Fourches, D. and Varnek, A. (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling, 48, 1733–1746.
  69. Todeschini, R., Ballabio, D., Consonni, V. and Mauri, A. (2008) A new similarity/diversity measure for the characterization of DNA sequences. CroaticaChemica Acta, 81, 657–664.
  70. Ballabio, D., Consonni, V. and Todeschini, R. (2007) Classification of multiway analytical data based on MOLMAP approach. Anal. Chim. Acta, 605, 134–146.
  71. Ballabio, D., Kokkinofta, R., Todeschini, R. and Theocharis, C.R. (2007) A classification model built by means of Artificial Neural Networks for the characterization of the traditional Cypriot spirit Zivania. Chemometrics &Intell. Lab. Syst., 87, 78–84.
  72. Todeschini, R., Ballabio, D., Consonni, V., Mauri, A. and Pavan, M. (2007) CAIMAN (Classification And Influence Matrix Analysis): A new approach to the classification based on leverage-scaled functions. Chemometrics &Intell. Lab. Syst., 87, 3–17.
  73. Todeschini, R., Ballabio, D., Consonni, V. and Mauri, A. (2007) A new similarity/diversity measure for sequential data. MATCH Commun. Math. Comput. Chem., 57, 51–67.
  74. Ballabio, D., Mauri, A., Todeschini, R. and Buratti, S. (2006) Geographical classification of wine and olive oil by means of CAIMAN (Classification And Influence Matrix Analysis). Anal. Chim. Acta, 570, 249–258.
  75. Ballabio, D., Cosio, M.S., Mannino, S. and Todeschini, R. (2006) A chemometric approach based on a novel similarity/diversity measure for the characterization and selection of electronic nose sensors. Anal. Chim. Acta, 578, 170–177.
  76. Mauri, A., Consonni, V., Pavan, M. and Todeschini, R. (2006) DRAGON software: an easy approach to molecular descriptor calculations. MATCH Commun. Math. Comput. Chem., 56, 237–248.
  77. Pavan, M., Consonni, V., Gramatica, P. and Todeschini, R. (2006) New QSAR modelling approach based on ranking models by Genetic Algorithms - Variable Subset Selection (GA-VSS), in Partial Order in Environmental Sciences and Chemistry (eds. R. Brüggeman and L. Carlsen), SpringerVerlag, pp. 185–224.
  78. Todeschini, R. (2006) Molecular Descriptors and Chemometrics. G. I. T. Laboratory Journal, 5, 40–42.
  79. Todeschini, R., Consonni, V., Mauri, A. and Ballabio, D. (2006) Characterization of DNA primary sequences by a new similarity/diversity measure based on the partial ordering. Journal of Chemical Information and Modeling, 46, 1905–1911.
  80. Pavan, M., Consonni, V. and Todeschini, R. (2005) Partial Ranking Models by Genetic Algorithms Variable Subset Selection (GA-VSS) approach for environmental priority settings. MATCH Commun. Math. Comput. Chem., 54, 583–609.
  81. Tetko, I.V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V.A., Radchenko, E.V., Zefirov, N.S., Makarenko, A.S., Tanchuk, V.Y. and Prokopenkov, V.V. (2005) Virtual ComputationalChemistryLaboratory -- Design and Description. J. Computer-AidedMol. Des., 19, 453–463.
  82. Pavan, M. and Todeschini, R. (2004) New indices for analyzing partial ranking diagrams. Anal. Chim. Acta, 515, 167–181.
  83. Pavan, M., Mauri, A. and Todeschini, R. (2004) Total ranking models by the Genetic Algorithms Variable Subset Selection (GA-VSS) approach for environmental priority settings. Analytical and BioanalyticalChemistry, 380, 430–444.
  84. Todeschini, R., Consonni, V., Mauri, A. and Pavan, M. (2004) New fitness functions to avoid bad regression models in variable subset selection by Genetic Algorithms, (eds. M. Ford, D. Livingstone, J. Deardean and H. van de Waterbeemd), Blakwell, Oxford (UK), pp. 323–325.
  85. Todeschini, R., Consonni, V., Mauri, A. and Pavan, M. (2004) Detecting "bad" regression models: multicriteria fitness functions in regression analysis. Anal. Chim. Acta, 515, 199–208.
  86. Todeschini, R., Consonni, V. and Pavan, M. (2004) A Distance Measure between Models: a Tool for Similarity/Diversity Analsysis of Model Populations. Chemometrics &Intell. Lab. Syst., 70, 55–61.
  87. Backhaus, T., Altenburger, R., Arrhenius, A., Blanck, H., Faust, M., Finizio, A., Gramatica, P., Grothe, M., Junghans, M., Meyer, W., Pavan, M., Porspring, T., Scholze, M., Todeschini, R., Vighi, M., Walter, H. and Grimme, L.H. (2003) The BEAM-project: prediction and assessment of mixture toxicities in the aquatic environment. Continental ShelfResearch, 23, 1757–1769.
  88. Lleti, R., Sarabia, L., Ortiz, M.C., Todeschini, R. and Colombini, M.P. (2003) Application of the Kohonen Artificial Neural Network in the identification of Proteinaceous Binders in Samples of Panel Painting Using Gas Chromatography-Mass Spectrometry. The Analyst, 181, 281–286.
  89. Mezzanotte, V., Castiglioni, F., Todeschini, R. and Pavan, M. (2003) Study on anaerobic and aerobic degradation of different non-ionic surfactants. Bioresource Technology, 87, 87–91.
  90. Todeschini, R., Consonni, V. and Pavan, M. (2003) MobyDigs: Software for Regression and Classification Models by Genetic Algorithms, in Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks (ed. R. Leardi), Elsevier, Amsterdam (The Netherlands), pp. 141–167.
  91. Todeschini, R. and Consonni, V. (2003) Descriptors from Molecular Geometry, in Handbook of Chemoinformatics - Vol.3, Vol. 3 (ed. J. Gasteiger), WILEY-VCH, Weinheim (GER), pp. 1004–1033.
  92. Todeschini, R., Consonni, V. and Pavan, M. (2003) Distance measure between models: a tool for model similarity/diversity analysis, in Designing Drugs and Crop Protectants: processes, problems and solutions. (eds. M. Ford, D. Livingstone, J. Deardean and H. van de Waterbeemd), Blakwell, Oxford (UK), pp. 467–469.
  93. Consonni, V., Todeschini, R. and Pavan, M. (2002) Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors. Journal of Chemical Information and Computer Sciences, 42, 682–692.
  94. Consonni, V., Todeschini, R., Pavan, M. and Gramatica, P. (2002) Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 2. Application of the Novel 3D Molecular Descriptors to QSAR/QSPR Studies. Journal of Chemical Information and Computer Sciences, 42, 693–705.
  95. Benicori, T., Consonni, V., Gramatica, P., Pilati, T., Rizzo, S., Sannicolò, F., Todeschini, R. and Zotti, G. (2001) Steric Control of Conductivity in Highly ConjugatedPolythiophenes. Chemistry of Materials, 13, 1665–1673.
  96. Di Marzio, W., Galassi, S., Todeschini, R. and Consolaro, F. (2001) Traditional versus WHIM molecular descriptors in QSAR approaches applied to fish toxicity studies. Chemosphere, 44, 401–406.
  97. Gramatica, P., Vighi, M., Consolaro, F., Todeschini, R., Finizio, A. and Faust, M. (2001) QSAR approach for the selection of congeneric compounds with a similar toxicological mode of action. Chemosphere, 42, 873–883.
  98. Vighi, M., Gramatica, P., Consolaro, F. and Todeschini, R. (2001) QSAR and Chemometric Approaches for Setting Water Quality Objectives for Dangerous Chemicals. Ecotoxicology and EnvironmentalSafety, 49, 206–220.
  99. Capitan-Vallvey, L.F., Navas, N., del Olmo, M., Consonni, V. and Todeschini, R. (2000) Resolution of mixtures of three nonsteroidal anti-inflammatory drugs by fluorescence using partial least squares multivariate calibration with previous wavelength selection by Kohonen artificial neural networks. Talanta, 52, 1069–1079.
  100. Todeschini, R. and Consonni, V. (2000) Handbook of Molecular Descriptors, Wiley-VCH, Weinheim (Germany), 668 pp.
Iranian Journal of Mathematical Chemistry
Volume 8, Issue 2
Special Issue of IJMC on Chemometrics
June 2017
Pages 93-105

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