A. A. Toropov; E. Benfenati, Optimisation of correlation weights of
SMILES invariants for modelling oral quail toxicity . European Journal
of Medicinal Chemistry (2007) 42( 5): 606-613.
A. A. Toropov; E. Benfenati, SMILES as an alternative to the graph in
QSAR modelling of bee toxicity . Computational Biology and Chemistry
(2007) 31(1): 57-60.
A. A. Toropov; A. P. Toropova ; E. Benfenati ; A. Manganaro, QSAR
modelling of carcinogenicity by balance of correlations. Mol Divers.
(2009) 13: 367–373.
A.A. Toropov; A. P. Toropova; E. Benfenati; D. Leszczynska; J.
Leszczynski, Additive InChI-based optimal descriptors: QSPR modeling of
fullerene C60 solubility in organic solvents. J Math Chem (2009) 46:
1232–1251.
A. A. Toropov; A. P. Toropova ; E. Benfenati, Additive SMILES-Based
Carcinogenicity Models: Probabilistic Principles in the Search for
Robust Predictions. Int. J. Mol. Sci. (2009) 10(7): 3106-3127.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modeling
bioconcentration factor (BCF) by balance of correlations . European
Journal of Medicinal Chemistry (2009) 44( 6): 2544-2551.
A. A. Toropov; A. P. Toropova ; E. Benfenati, SMILES-based optimal
descriptors: QSAR modeling of carcinogenicity by balance of
correlations with ideal slopes. European Journal of Medicinal Chemistry
(2010) 45: 3581-3587.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modeling for
enthalpies of formation of organometallic compounds by means of
SMILES-based optimal descriptors . Chemical Physics Letters (2008) 461(
4-6): 343-347.
A. A. Toropov, B.F. Rasulev, D. Leszczynska, J. Leszczynski,
Multiplicative SMILES-based optimal descriptors: QSPR modeling of
fullerene C60 solubility in organic solvents . Chemical Physics Letters
(2008) 457( 4-6): 332-336.
A. A. Toropov; E. Benfenati, Additive SMILES-based optimal descriptors
in QSAR modelling bee toxicity: Using rare SMILES attributes to define
the applicability domain. Bioorganic & Medicinal Chemistry
(2008 )16( 9): 4801-4809.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modelling of the
octanol/water partition coefficient of organometallic substances by
optimal SMILES-based descriptors. Cent. Eur. J. Chem. 2009 7(4):
846–856.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modeling of octanol
water partition coefficient of platinum complexes by InChI-based
optimal descriptors. J Math Chem (2009) 46: 1060–1073.
A.A. Toropov; A. P. Toropova; E. Benfenati; D. Leszczynska; J.
Leszczynski, Additive InChI-based optimal descriptors: QSPR modeling of
fullerene C60 solubility in organic solvents. J Math Chem (2009) 46:
1232–1251.
A. A. Toropov; A. P. Toropova ; E. Benfenati, Additive SMILES-Based
Carcinogenicity Models: Probabilistic Principles in the Search for
Robust Predictions. Int. J. Mol. Sci. (2009) 10(7): 3106-3127.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modeling
bioconcentration factor (BCF) by balance of correlations . European
Journal of Medicinal Chemistry (2009) 44( 6): 2544-2551.
A. A. Toropov; A. P. Toropova ; E. Benfenati, Simplified Molecular
Input Line Entry System-Based Optimal Descriptors: Quantitative
Structure–Activity Relationship Modeling Mutagenicity of Nitrated
Polycyclic Aromatic Hydrocarbons. Chemical Biology & Drug
Design ( 2009) 73(5): 515–525.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSAR Modelling for
Mutagenic Potency of Heteroaromatic Amines by Optimal SMILES-based
Descriptors. Chemical Biology & Drug Design ( 2009) 73(3):
301–312.
A. A. Toropov; A. P. Toropova ; E. Benfenati, Erratum: QSAR Modelling
for Mutagenic Potency of Heteroaromatic Amines by Optimal SMILES-based
Descriptors. Chemical Biology & Drug Design ( 2009) 73(4): 482.
A. A. Toropov; A. P. Toropova ; E. Benfenati ; A. Manganaro, QSAR
modelling of carcinogenicity by balance of correlations. Mol Divers
(2009) 13:367–373.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modelling of normal
boiling points and octanol/water partition coefficient for acyclic and
cyclic hydrocarbons using SMILES-based optimal descriptors . Central
European Journal Chemistry. (2010) 8: 1047-1062.
A. A. Toropov; A. P. Toropova ; E. Benfenati; D. Leszczynska; J.
Leszczynski, SMILES-based optimal descriptors: QSAR analysis of
fullerene-based HIV-1 PR inhibitors by means of balance of
correlations. J Comput Chem. (2010) 31 : 381-392.
A. A. Toropov; A. P. Toropova ; E. Benfenati; D. Leszczynska; J.
Leszczynski, QSAR analysis of
1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines exhibiting
anticancer activity by optimal SMILES-based descriptors. J Math Chem.
(2010) 47 : 647-666.
A. A. Toropov; A. P. Toropova ; E. Benfenati ; A. Manganaro, QSAR
modelling of the toxicity to Tetrahymena pyriformis by balance of
correlations. Mol Divers. (2010) 14 : 821-827.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSAR-modeling of toxicity
of organometallic compounds by means of the balance of correlations for
InChI-based optimal descriptors. Mol Divers. (2010) 14 : 183-192.
A. A. Toropov; A. P. Toropova ; E. Benfenati; D. Leszczynska; J.
Leszczynski, Use of the international chemical identifier for
constructing QSPR-model of normal boiling points of acyclic carbonyl
substances. J Math Chem. (2010) 47: 355-369.
A. A. Toropov; A. P. Toropova ; E. Benfenati; D. Leszczynska; J.
Leszczynski, QSAR modeling of measured binding affinity for
fullerene-based HIV-1 PR inhibitors by CORAL. J Math Chem. (2010) 48 :
959-987.
A. A. Toropov; A. P. Toropova ; E. Benfenati, SMILES-based optimal
descriptors: QSAR modeling of carcinogenicity by balance of
correlations with ideal slopes. European Journal of Medicinal Chemistry
(2010) 45: 3581-3587.
A. A. Toropov; A. P. Toropova ; E. Benfenati; D. Leszczynska; J.
Leszczynski, InChI-based optimal descriptors: QSAR analysis of
fullerene [C60]-based HIV-1 PR inhibitors by correlation balance. Eur J
Med Chem. (2010) 45 : 1387-1394.
A. A. Toropov; A. P. Toropova ; I.Jr.Raska; E. Benfenati, QSPR modeling
of octanol/water partition coefficient of antineoplastic agents by
balance of correlations. Eur J Med Chem. (2010) 45 : 1639-1647.
A. P. Toropova ; A. A. Toropov; E. Benfenati; G. Gini; D. Leszczynska;
J. Leszczynski, CORAL: QSPR models for solubility of [C60] and [C70]
fullerene derivatives Mol. Divers. (2011) 105: 249-256.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors. Cent. Eur. J. Chem. 2009 7(4): 846–856.
A. A. Toropov; A. P. Toropova ; E. Benfenati, QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors. Mol Divers. (2010) 14 : 183-192.
A. P. Toropova ; A. A. Toropov; E. Benfenati; G. Gini, QSAR modelling toxicity toward rats of inorganic substances by means of CORAL. Cent. Eur. J. Chem. (2011) 9(1): 75-85.
A. P. Toropova ; A. A. Toropov; A. Lombardo; A. Roncaglioni; E. Benfenati; G. Gini, A new bioconcentration factor model based on SMILES and indices of presence of atoms. Eur J Med Chem. (2010) 45 : 4399-4402.
A. P. Toropova ; A. A. Toropov; R. Gonella Diaza; E. Benfenati; G. Gini, Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: an unexpected good prediction based on a model that seems untrustworthy. Central European Journal Chemistry (2011) 9 : 165-174.
A. P. Toropova ; A. A. Toropov; E. Benfenati; G. Gini, Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy. Chemometr. Intell. Lab. (2011) 105: 215-219.
A.A. Toropov; A.P. Toropova; A. Lombardo; A. Roncaglioni; E. Benfenati; G. Gini, CORAL: building up the model for bioconcentration factor and defining it’s applicability domain Eur. J. Med. Chem. (2011) 46: 1400-1403.
A. A. Toropov ; A. P. Toropova; R. Gonella Diaza; E. Benfenati; G. Gini, SMILES-based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation balance. Struct Chem. (2012) 23: 529-544.
A.P. Toropova; A.A. Toropov; A. Lombardo; A. Roncaglioni; E. Benfenati; G. Gini, Coral: QSAR models for acute toxicity in fathead minnow (Pimephales promelas). J Comput Chem. (2012) 33 : 1218-1223.
A.P. Toropova; A.A. Toropov; S.E. Martyanov; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: QSAR modeling of toxicity of organic chemicals to Daphnia magna. Chemometr. Intell. Lab. (2012) 110: 177-181.
A.P. Toropova; A.A. Toropov; A. Lombardo; A. Roncaglioni; E. Benfenati; G. Gini, Coral: QSAR models for acute toxicity in fathead minnow (Pimephales promelas). J Comput Chem. (2012) 33 : 1218-1223.
A.P. Toropova; A.A. Toropov; E. Benfenati; G. Gini, QSAR models for toxicity of organic substances to Daphnia magna built up by CORAL freeware. Chem Biol Drug Des. 2012; 79: 332–338.
A.A. Toropov, A.P. Toropov, S.E. Martyanov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, CORAL: Predictions of Rate Constants of Hydroxyl Radical Reaction using representation of the molecular structure obtained by combination of SMILES and Graph approaches. Chemometr. Intell. Lab. (2012) 112: 65-70.
A.A. Toropov; A.P. Toropova; A. Lombardo; A. Roncaglioni; N. De Brita; G. Stella; E. Benfenati, CORAL: the prediction of biodegradation of organic compounds with optimal SMILES-based descriptors. Cent. Eur. J. Chem. (2012) 10: 1042-1048.
A.P. Toropova; A.A. Toropov; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, The average numbers of outliers over groups of various splits into training and test sets: a criterion of the reliability of a QSPR? A case of water solubility. Chem. Phys. Lett. (2012) 542: 134-137.
A. A. Toropov; A.P. Toropova; B.F. Rasulev; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical. J. Comput. Chem. (2012) 33: 1902-1906.
A. A. Toropov; A.P. Toropova; B.F. Rasulev; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents. Anti-Cancer Agents in Med. Chem. (2012) 12: 807-817.
A. A. Toropov; A.P. Toropova; E. Benfenati; G. Gini; T. Puzyn; D. Leszczynska; J. Leszczynski, Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere (2012) 89: 1098–1102.
A. P. Toropova; A.A. Toropov; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: Quantitative Models for Estimatingbioconcentration Factor of Organic Compounds. Chemometr. Intell. Lab. (2012) 118: 70-73.
A. P. Toropova; A.A. Toropov; B.F. Rasulev; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES. Struct Chem . (2012) 23: 1873-1878.
A. A. Toropov; A.P. Toropova; I. Raska Jr ; E. Benfenati; G. Gini, QSAR modeling of endpoints for peptides which is based on representation of the molecular structure by a sequence of amino acids. Struct Chem. (2012) 23: 1891-1904.
A. P. Toropova; A.A. Toropov; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: QSAR models of toxicity of binary mixtures . Chemometr. Intell. Lab. (2012) 119: 39-43.
A. A. Toropov; A.P. Toropova; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: Classification model for predictions of anti-sarcoma activity. Current Topics in Medicinal Chemistry (2012) 12: 2741-2744.
A. A. Toropov; A.P. Toropova; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: QSPR model of water solubility based on local and global SMILES attributes. Chemosphere (2013) 90: 877–880.
A. P. Toropova; A.A. Toropov; S.E. Martyanov; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: the Monte Carlo method as a tool for prediction of bioconcentration factor of industrial pollutants. Molecular Informatics (2013) 32: 145-154.
A. A. Toropov; A.P. Toropova; E. Benfenati; G. Gini; R. Fanelli, The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method. Struct Chem. (2013) 24: 1369-1381.
A. A. Toropov; A.P. Toropova ; I. Raska Jr ; E. Benfenati ; G. Gini , Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method. Cent. Eur. J. Chem. (2013) 11: 371-380.
A. P. Toropova; A.A. Toropov; T. Puzyn; E. Benfenati; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of Micro-Electro-Mechanical Systems. J. Math. Chem. (2013) 51: 2230-2237.
A. P. Toropova; A.A. Toropov; T. Puzyn; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, QSAR as a random event: Models for nanoparticles uptake in PaCa2 cancer cells. Chemosphere (2013) 92: 31–37.
A. P. Toropova; A.A. Toropov; T. Puzyn; E. Benfenati; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of Micro-Electro-Mechanical Systems. J. Math. Chem. (2013) 51: 2230-2237.
A. P. Toropova; A.A. Toropov, Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO2 nanoparticles. Chemosphere (2013) 93: 2650–2655.
A. A. Toropov; A.P. Toropova, Optimal descriptor as a translator of eclectic data into endpoint prediction: Mutagenicity of fullerene as a mathematical function of conditions. Chemosphere (2014) 104: 262-264.
A. P. Toropova ; A.A. Toropov; E. Benfenati; T. Puzyn; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: The case of a group of ZnO and TiO2 nanoparticles. Ecotoxicology and Environmental Safety (2014) 108: 203-209.
A. Worachartcheewan ; P. Mandi ; V. Prachayasittikul ; A.P. Toropova ; A.A. Toropov ; C. Nantasenamat , Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors . Chemometrics and Intelligent Laboratory Systems (2014) 138: 120-126.
P.R. Duchowicz ; S.E. Fioressi; D.E. Bacelo; L.M. Saavedra; A.P. Toropova; A.A. Toropov, QSPR Studies on Refractive Indices of Structurally Heterogeneous Polymers. Chemom. Intell. Lab. Syst. (2015) 140: 86–91.
J. B. Veselinović A. A. Toropov; A.P. Toropova; G. M. Nikolić A. M. Veselinović, Monte Carlo Method-Based QSAR Modeling of Penicillins Binding to Human Serum Proteins. Arch. Pharm. (2015) 348(1): 62-67.
A. P. Toropova; A. A. Toropov; R. Rallo; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicology and Environmental Safety (2015) 112: 39–45.
A. A. Toropov, A.P. Toropova. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere (2015) 124 ; 40–46
A. P. Toropova and A. A. Toropov. Mutagenicity: QSAR - quasi-QSAR - nano-QSAR. Mini-Reviews in Medicinal Chemistry, (2015) Vol. 15, No. 2 , 608-621
A. P. Toropova, A. A. Toropov, J.B. Veselinović, A. M. Veselinović, E. Benfenati, D. Leszczynska, J. Leszczynski. Application of the Monte Carlo method to prediction of dispersibility of graphene in various solvents. Int. J. Environ. Res., 9(4):1211-1216, Autumn 2015
A. A. Toropov, A. P. Toropova, A. M. Veselinović, J. B. Veselinović, K. Nesměrák, I. Raska Jr, P. R. Duchowicz, E. A. Castro,
V. O. Kudyshkin, D. Leszczynska, J. Leszczynski. The Monte Carlo method based on eclectic data as an efficient tool
for predictions of endpoints for nanomaterials – two examples of application. Combinatorial Chemistry & High Throughput Screening. 2015, 18(4), 376-386
A. P. Toropova, A. A. Toropov, Quasi-SMILES and nano-QFAR: United model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere, 139 (2015) 18-22
Alla P. Toropova, Andrey A. Toropov, Valentin O. Kudyshkin, Robert Rallo, Prediction of the Q-e parameters from structures of transfer chain agents, Journal of Polymer Research, 22 (2015)128.
Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E. (2015): QSAR model for cytotoxicity of silica nanoparticles on human embryonic kidney cells. Materials Today: Proceedings , Volume 3, Issue 3, 2016, Pages 847-854.
S. Manganelli, C. Leone, A.A. Toropov, A.P. Toropova, E. Benfenati, QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles. Chemosphere,144 (2016) 995-1001.
Veselinovic A.M., Veselinovic J.B., Nikolic G.M., Toropova A.P., Toropov A.A., QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method. Struct. Chem. (2016) 27: 821–828.
Toropov A.A., Toropova A.P., Nesměrák K., Veselinović A.M., Veselinović J.B., Leszczynska D., Leszczynski J.: Development of the latest tools for building up "nano-QSAR": Quantitative features-property/activity relationships (QFPRs/QFARs).
Chapter 12, In Book: Practical Aspects of Computational Chemistry IV. J. Leszczynski, M.K. Shukla (Eds.). Springer 2016. ISBN 78-1-4899-7699-4, p. 353-396. DOI: 10.1007/978-1-4899-7699-4_12 http://rd.springer.com/chapter/10.1007%2F978-1-4899-7699-4_12
A. P. Toropova; A.A. Toropov; T. Puzyn; E. Benfenati; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of Micro-Electro-Mechanical Systems. J. Math. Chem. (2013) 51: 2230-2237.
A.P. Toropova; A.A. Toropov; E. Benfenati ; R. Korenstein , QSAR model for cytotoxicity of SiO2 nanoparticles on human lung fibroblasts , (2014) Journal of Nanoparticle Research 16: 2282.
A. Worachartcheewan ; P. Mandi ; V. Prachayasittikul ; A.P. Toropova ; A.A. Toropov ; C. Nantasenamat , Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors. Chemometrics and Intelligent Laboratory Systems (2014) 138: 120-126.
A. P. Toropova; A.A. Toropov; R. Rallo; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicology and Environmental Safety (2015) 112: 39–45.
A.P. Toropova; A.A. Toropov; E. Benfenati; R. Korenstein; D. Leszczynska; J. Leszczynski, Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides. Environ Sci Pollut Res. (2015) 22:745–757
A. A. Toropov, A.P. Toropova. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere (2015) 124 ; 40–46
A. P. Toropova and A. A. Toropov. Mutagenicity: QSAR - quasi-QSAR - nano-QSAR. Mini-Reviews in Medicinal Chemistry, (2015) Vol. 15, No. 2, 608-621
A. A. Toropov, A. P. Toropova, A. M. Veselinovic´, J. B. Veselinovic´, K. Nesměrák, I. Raška Jr, P. R. Duchowicz, E. A. Castro,
V. O. Kudyshkin, D. Leszczynska, J. Leszczynski. The Monte Carlo method based on eclectic data as an efficient tool
for predictions of endpoints for nanomaterials – two examples of application. Combinatorial Chemistry & High Throughput Screening. 2015, 18(4), 376-386
A.P. Toropova, A. A. Toropov, E. Benfenati, A quasi-QSPR modeling for the photocatalytic decolorisation rate constants and cellular viability (CV%) of nanoparticles by CORAL. SAR and QSAR in Environmental Research. Jan 2015, 26(1); 29-40.
Alla P. Toropova, Andrey A. Toropov, Valentin O. Kudyshkin, Robert Rallo, Prediction of the Q-e parameters from structures of transfer chain agents, Journal of Polymer Research, 22 (2015)128.
A. P. Toropova, A. A. Toropov, Quasi-SMILES and nano-QFAR: United model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere, 139 (2015) 18-22
A. P. Toropova, A. A. Toropov, J.B. Veselinović, A. M. Veselinović, E. Benfenati, D. Leszczynska, J. Leszczynski. Application of the Monte Carlo method to prediction of dispersibility of graphene in various solvents. Int. J. Environ. Res., 9(4):1211-1216, Autumn 2015
Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E. QSAR model for cytotoxicity of silica nanoparticles on human embryonic kidney cells. Materials Today: Proceedings, Volume 3, Issue 3, 2016, Pages 847-854.
S. Manganelli, C. Leone, A.A. Toropov, A.P. Toropova, E. Benfenati, QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles. Chemosphere,144 (2016) 995-1001.
Veselinovic A.M., Veselinovic J.B., Nikolic G.M., Toropova A.P., Toropov A.A., QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method. Struct. Chem. (2016) 27: 821–828.
Toropov A.A., Toropova A.P., Nesměrák K., Veselinović A.M., Veselinović J.B., Leszczynska D., Leszczynski J.: Development of the latest tools for building up "nano-QSAR": Quantitative features-property/activity relationships (QFPRs/QFARs).
Chapter 12, In Book: Practical Aspects of Computational Chemistry IV. J. Leszczynski, M.K. Shukla (Eds.). Springer 2016. ISBN 78-1-4899-7699-4, p. 353-396. DOI: 10.1007/978-1-4899-7699-4_12 http://rd.springer.com/chapter/10.1007%2F978-1-4899-7699-4_12
A. A. Toropov; A.P. Toropova ; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski; G.De Nucci , QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea. Biochemical and Biophysical Research Communications (2013) 432: 214–225.
A. P. Toropova; A.A. Toropov; E. Benfenati; G. Gini; D. Leszczynska; J. Leszczynski, CORAL: QSPRs of enthalpies of formation of organometallic compounds. J Math Chem. (2013) 51: 1684-1693.
A. A. Toropov; A.P. Toropova ; E. Benfenati; G. Gini, OCWLGI Descriptors: Theory and Praxis. Curr Comput-Aid Drug Des. (2013) 9: 226-232.
A. Worachartcheewan ; P. Mandi ; V. Prachayasittikul ; A.P. Toropova ; A.A. Toropov ; C. Nantasenamat , Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors. Chemometrics and Intelligent Laboratory Systems (2014) 138: 120-126.
Veselinovic A.M., Veselinovic J.B., Nikolic G.M., Toropova A.P., Toropov A.A., QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method. Struct. Chem. (2016) 27: 821–828.
A. P. Toropova; A.A.Toropov; R. Rallo; D. Leszczynska; J. Leszczynski, Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicology and Environmental Safety (2015) 112: 39–45.
A. A. Toropov, A.P. Toropova. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere (2015) 124 ; 40–46
A.P. Toropova , A. A. Toropov , E.Benfenati , D. Leszczynska , J. Leszczynski. QSAR model as a random event: A case of rat toxicity. Bioorganic & Medicinal Chemistry 2015, 23(6), 1223-1230
Andrey A. Toropov, Alla P. Toropova, Claudia Ileana Cappelli, Emilio Benfenati. CORAL: model for octanol/water partition coefficient. Fluid Phase Equilibria, (2015); 397, 44-49
Andrey A. Toropov, Alla P. Toropova, Fabiola Pizzo, Anna Lombardo, Domenico Gadaleta, Emilio Benfenati. CORAL: Model for No Observed Adverse Effect Level (NOAEL). Molecular Diversity, 19(3) (2015) 563-575.
A. P. Toropova, A. A. Toropov, J. B. Veselinović, A. M. Veselinović, QSAR as a random event: a case of NOAEL. Environ. Sci. Poll. Res. (2015), 22(11), 8264-8271
A. A. Toropov, R. Rallo, A. P. Toropova. Use of quasi-SMILES and Monte Carlo optimization to develop quantitative feature property/activity relationships(QFPR/QFAR) for nanomaterials. Current Topics in Medicinal Chemistry, 2015, 15( 18 )1837-1844.
Alla P. Toropova, Andrey A. Toropov, Valentin O. Kudyshkin, Robert Rallo, Prediction of the Q-e parameters from structures of transfer chain agents, Journal of Polymer Research, J. Polym. Res. 22 (2015)128
M. A. Toropova, A. A. Toropov, I. Raška Jr, M. Rašková. Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method. Computers in Biology and Medicine,64 (1 September 2015 ) 148-154.
A. P. Toropova, A. A. Toropov, Quasi-SMILES and nano-QFAR: United model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere, 139 (2015) 18-22
Apilak Worachartcheewan, Virapong Prachayasittikul, Alla P. Toropova, Andrey A. Toropov, Chanin Nantasenamat, Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors. Molecular Diversity, 2015; 19: 955-964.
A. P. Toropova, A. A. Toropov, J.B. Veselinović, A. M. Veselinović, E. Benfenati, D. Leszczynska, J. Leszczynski. Application of the Monte Carlo method to prediction of dispersibility of graphene in various solvents. Int. J. Environ. Res., 9(4): 1211-1216, Autumn 2015
A. P. Toropova, A.A. Toropov, and E. Benfenati, CORAL: Prediction of binding affinity and efficacy of thyroid hormone receptor ligands. Journal of Medicinal Chemistry, 101 (2015) 452-461.
Alla P. Toropova, Andrey A. Toropov, Aleksandar M. Veselinović, Jovana B. Veselinović, Emilio Benfenati, Danuta Leszczynska, Jerzy Leszczynski, Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicology and Environmental Safety, 124 (2016) 32-36.
Karel Nesměrák, Andrey A. Toropov, Alla P. Toropova, Ilkay Yildiz, Ismail Yalcin, Marketa Brozikova, Vera Klimešová, Karel Waisser, Prediction of Retention Characteristics of Heterocyclic Compounds. Analytical and Bioanalytical Chemistry,(2015) 407: 9185–9189.
Alla P. Toropova, Terry W. Schultz, Andrey A. Toropov, Building up a QSAR model for toxicity towards Tetrahymena Pyriformis by the Monte Carlo method: A case of benzene derivatives. Environmental Toxicology and Pharmacology, 42 (2016) 135-145.
Karel Nesměrák, Andrey A. Toropov, Alla P. Toropova, Model for electrochemical parameters for 4-(benzylsulfanyl)pyridines calculated from the molecular structure. Journal of Electroanalytical Chemistry. 766 (2016) 24-29.
Toropova, A.; Toropov, A. CORAL: The dispersion of SWNTs in different organic solvents. In Proceedings of the MOL2NET, 5-15 December 2015; Sciforum Electronic Conference Series, Vol. 1, 2015 , c007; doi:10.3390/MOL2NET-1-c007.
Toropova, A.P., Toropov, A.A., Rallo, R., Leszczynska, D., and Leszczynski, J.,Nano-QSAR: Genotoxicity of multi-walled carbon nanotubes. Int. J. Environ. Res., 10(1): 59-64, Winter 2016.
Alla P. Toropova, Andrey A. Toropov. QSPR model for dispersibility of graphene in various solvents, Letters in Drug Design & Discovery, 13( 6), 2016, 514-520.
Alla P. Toropova, P.Ganga Raju Achary, Andrey A. Toropov. Quasi-SMILES for Nano-QSAR prediction of toxic effect of Al2O3 nanoparticles. Journal of Nanotoxicology and Nanomedicine, 1(1), 2016, 17-28.
Andrey A. Toropov, Alla. P. Toropova, Emilio Benfenati, Roberto Fanelli, QSAR as a random event: selecting of the molecular structure for potential anti-tuberculosis agents, Anti-Infective Agents, 2016, 14(1): 3 - 10.
Alla P. Toropova, Andrey A. Toropov, Evolution of Optimal Descriptors: Solved, Unsolved, and Unsoluble Tasks, International Journal of Quantitative Structure-Property Relationships , 1 (2), 2016, 52-71.
Alla P. Toropova and Andrey A. Toropov, Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies. Materials Discovery, 4 (2016) 22–28.
Toropov A.A., Toropova A.P., Nesměrák K., Veselinović A.M., Veselinović J.B., Leszczynska D., Leszczynski J.: Development of the latest tools for building up "nano-QSAR": Quantitative features-property/activity relationships (QFPRs/QFARs).
Chapter 12, In Book: Practical Aspects of Computational Chemistry IV. J. Leszczynski, M.K. Shukla (Eds.). Springer 2016. ISBN 78-1-4899-7699-4, p. 353-396. DOI: 10.1007/978-1-4899-7699-4_12 http://rd.springer.com/chapter/10.1007%2F978-1-4899-7699-4_12
Andrey A. Toropov, Alla P. Toropova, Francesca Como, Emilio Benfenati; Quantitative structure–activity relationship models for bee toxicity. Toxicological & Environmental Chemistry; 99: 7-8, 2017, 1117-1128. DOI: 10.1080/02772248.2016.1242006
Veda Prachayasittikula, Apilak Worachartcheewana, Alla P. Toropova, Andrey A. Toropov, Virapong Prachayasittikul, Chanin Nantasenamat. Large-scale classification of P -glycoprotein inhibitors using SMILES-based descriptors. SAR and QSAR in Environmental Research, 28(1), 2017, 1-16. DOI:10.1080/1062936X.2016.1264468
J. B. Veselinović, A. M. Veselinović, A. P. Toropova, A. A. Toropov, The Monte Carlo technique as a tool to predict LOAEL. European Journal of Medicinal Chemistry, 116 (2016) 71-75.
Andrey A. Toropov, Alla P. Toropova, Sanija Begum, P. Ganga Raju Achary, Towards predicting the solubility of CO2 and N2 in different polymers using a Quasi-SMILES based QSPR approach. SAR and QSAR in Environmental Research,Vol. 27, Iss. 4, 2016, pages 293-301.
M. Gobbi, M. Beeg, M. A. Toropova, A. A. Toropov, M. Salmona, Monte Carlo method for Predicting of Cardiac Toxicity: hERG blocker compounds. Toxicology Letters. 250 (2016) 42–46.
A. P. Toropova, A. A. Toropov, S. Manganelli, C.Leone, D. Baderna, E. Benfenati, R. Fanelli, Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials. NANOIMPACT, 1 (2016) 60-64.
A. P. Toropova, A. A. Toropov, A. M. Veselinović, J. B. Veselinović, D. Leszczynska, J. Leszczynski, Monte Carlo based QSAR models for toxicity of organic chemicals to Daphnia magna. Environmental Toxicology and Chemistry, Vol. 35, No. 11, pp. 2691–2697, 2016. DOI: 10.1002/etc.3466
Alla P. Toropova, P.Ganga Raju Achary, Andrey A. Toropov. Quasi-SMILES for Nano-QSAR prediction of toxic effect of Al2O3 nanoparticles. Journal of Nanotoxicology and Nanomedicine, 1(1), 2016, 17-28.
M. A. Toropova, I. Raška Jr, A.A. Toropov, M. Rašková, The utilization of the Monte Carlo technique for rational drug discovery. Combinatorial Chemistry & High Throughput Screening. 2016, 19 (8), 676-687.
P. Ganga Raju Achary, Sanija Begum, Alla P. Toropova, Andrey A. Toropov, A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler - Natta catalysts for propylene polymerization. Materials Discovery, 5, August 2016, 22–28.
Andrey A. Toropov, P. Ganga Raju Achary, Alla P. Toropova. Quasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles. Chemical Physics Letters 660 (2016) 107-110.
A.A. Toropov, A. P. Toropova, L. Cappellini, E. Benfenati, E. Davoli. Odor Threshold prediction by means of the Monte Carlo method. Ecotoxicology and Environmental Safety 133 (2016) 390-394.
Alla P. Toropova and Andrey A. Toropov, Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies. Materials Discovery, 4 (2016) 22–28.
Alla P. Toropova, Andrey A. Toropov. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. Journal of Theoretical Biology 416 (2017) 113-118.
Alla P. Toropova, Andrey A. Toropov. CORAL: Binary classifications (active/inactive) for drug-induced liver injury. Toxicology Letters, 268, 15 February 2017, 51–57.
M. A. Toropova, I. Raska Jr, A. P. Toropova, M. Raskova. CORAL software: analysis of impacts of pharmaceutical agents upon metabolism via the optimal descriptors. Current Drug Metabolism, Vol. 18, No. 6, 500-510, 2017.
Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski. CORAL and Nano-QFAR: Quantitative feature – activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicology and Environmental Safety, 2017; 139: 404-407.
Toropova, A. P.; Toropov, A. A.; Veselinovic, A. M.; Veselinovic, J. B.; Leszczynska, D.; Leszczynski, J. Quasi-SMILES as a Novel Tool for Prediction of Nanomaterials' Endpoints. In Multi-Scale Approaches in Drug Discovery: From Empirical Knowledge to In Silico Experiments and Back, 1st ed.; Speck-Planche, A., Ed. Elsevier: Oxford, UK, 2017; pp 191-221. doi: http://dx.doi.org/10.1016/B978-0-08-101129-4.00008-4
Alla P. Toropova, Andrey A. Toropov, Marten Beeg, Marco Gobbi, Mario Salmona, Utilization of the Monte Carlo method to build up QSAR models
for hemolysis and cytotoxicity of antimicrobial peptides. Current Drug Discovery Technologies,14(4), 2017, 229-243.
DOI: 10.2174/1570163814666170525114128
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Robert Rallo, Danuta Leszczynska and Jerzy Leszczynski: Development of Monte Carlo Approaches in Support of Environmental Research.
Chapter 12 (pages 453-469), In Book: Advances in QSAR modeling. Volume 24 of the series Challenges and Advances in Computational Chemistry and Physics. Edited: Roy, K. Springer International Publishing AG, 25 May 2017. DOI: 10.1007/978-3-319-56850-8_12
Alla P. Toropova, Andrey A. Toropov, Maria Raškova, Ivan Raška Jr, Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method. Environmental Toxicology and Pharmacology 48 (2016) 278–285.
Alla P. Toropova, Andrey A. Toropov. CORAL: Binary classifications (active/inactive) for drug-induced liver injury. Toxicology Letters, 268, 15 February 2017, 51–57.
Alla P. Toropova, Andrey A. Toropov. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Science of the Total Environment 586 (2017) 466–472.
Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski. CORAL and Nano-QFAR: Quantitative feature – activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicology and Environmental Safety, 2017; 139: 404-407.
Alla P. Toropova, Andrey A. Toropov. Hybrid Optimal Descriptors as a Tool to Predict Skin Sensitization in accordance to OECD principles. Toxicology Letters 275 (2017) 57-66.
Andrey A. Toropov, Alla P. Toropova. The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models? Mutation Research - Genetic Toxicology and Environmental Mutagenesis, 819 (2017) 31–37.
Alla P. Toropova, Andrey A. Toropov, Marco Marzo, Sylvia E. Escher, Jean Lou Dorne,
Nikolaos Georgiadis, Emilio Benfenati. The application of new HARD-descriptor available from the CORAL software to building up NOAEL models.
Food and Chemical Toxicology 112 (2018) 544-550.
Cosimo Toma, Domenico Gadaleta, Alessandra Roncaglioni, Andrey Toropov, Alla Toropova, Marco Marzo, Emilio Benfenati,
QSAR development for plasma protein binding: influence of the ionization state.
Pharmaceutical Research,(2019) 36: 28.
Alla P. Toropova, Andrey A. Toropov, Marco Marzo, Sylvia E. Escher, Jean Lou Dorne, Nikolaos Georgiadis, Emilio Benfenati,
Corrigendum to “The application of new HARD-descriptor available from the CORAL software to building up NOAEL models”[Food Chem. Toxicol.112 (2018) 544–550].Food and Chemical Toxicology 128 (2019) 146.
Gadaleta, D.; Marzo, M.; Toropov, A.A.; Toropova, A.P.; Lavado, G.; Escher, S.; Dorne, J.-L.; Benfenati, E.
Integrated in silico models for the prediction of No-Observed-(Adverse)-Effect-Levels and Lowest-Observed-(Adverse)-Effect-Levels in rats for sub-chronic repeated dose toxicity.
Chemical Research in Toxicology, 2021, 34, 2, 247–257. https://doi.org/10.1021/acs.chemrestox.0c00176
Alla P. Toropova, Andrey A. Toropov, Maria Raškova, Ivan Raška Jr, Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method. Environmental Toxicology and Pharmacology 48 (2016) 278–285.
Alla P. Toropova, Andrey A. Toropov. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Science of the Total Environment 586 (2017) 466–472.
Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski. CORAL and Nano-QFAR: Quantitative feature – activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicology and Environmental Safety, 2017; 139: 404-407.
A. A. Toropov, A. P. Toropova, M. Marzo, J.L. Dorne, N. Georgiadis, E. Benfenati, QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database.
Environmental Toxicology and Pharmacology 53 (2017) 158–163.
Alla P. Toropova, Andrey A. Toropov, Marco Marzo, Sylvia E. Escher, Jean Lou Dorne,
Nikolaos Georgiadis, Emilio Benfenati. The application of new HARD-descriptor available from the CORAL software to building up NOAEL models.
Food and Chemical Toxicology 112 (2018) 544-550.
Gadaleta, D.; Marzo, M.; Toropov, A.A.; Toropova, A.P.; Lavado, G.; Escher, S.; Dorne, J.-L.; Benfenati, E.
Integrated in silico models for the prediction of No-Observed-(Adverse)-Effect-Levels and Lowest-Observed-(Adverse)-Effect-Levels in rats for sub-chronic repeated dose toxicity.
Chemical Research in Toxicology, 2021, 34, 2, 247–257. https://doi.org/10.1021/acs.chemrestox.0c00176
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Robert Rallo, Danuta Leszczynska and Jerzy Leszczynski: Development of Monte Carlo Approaches in Support of Environmental Research.
Chapter 12 (pages ),In Book: Advances in QSAR modeling. Edited: Roy, K. Springer International Publishing AG, 2017.
Alla P. Toropova, Andrey A. Toropov. Hybrid Optimal Descriptors as a Tool to Predict Skin Sensitization in accordance to OECD principles. Toxicology Letters 275 (2017)57-66.
Andrey A. Toropov, Alla P. Toropova, Marten Beeg, Marco Gobbi, Mario Salmona, QSAR model for Blood-Brain Barrier Permeation. Journal of Pharmacological and Toxicological Methods 88 (2017) 7–18.
Andrey A. Toropov, Alla P. Toropova. The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models? Mutation Research - Genetic Toxicology and Environmental Mutagenesis, 819 (2017) 31–37.
A. A. Toropov, A. P. Toropova, M. Marzo, J.L. Dorne, N. Georgiadis, E. Benfenati, QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database.
Environmental Toxicology and Pharmacology 53 (2017) 158–163.
Karel Nesmerák, Andrey A. Toropov, Alla P. Toropova, Tugba Ertan-Bolelli, Ilkay Yildiz. QSAR of antimycobacterial activity of benzoxazoles by optimal SMILES-based descriptors. Med. Chem. Res. (2017) 26: 3203-3208.
Alla P. Toropova, Andrey A. Toropov, CORAL: Monte Carlo method to predict endpoints for medical chemistry. Mini-Reviews in Medicinal Chemistry. 18(5), 2018, 382 - 391.
Alla P. Toropova, Andrey A. Toropov, S. Begum, P. G. R. Achary, Blood brain barrier and Alzheimer’s disease:
Similarity and dissimilarity of molecular alerts. Current Neuropharmacology, 2018, 16, 769-785.
Andrey A. Toropov, Alla P. Toropova, Luigi Cappellini, Emilio Benfenati, Enrico Davoli, QSPR analysis of threshold of odor for the large number of heterogenic chemicals. Molecular Diversity, (2018) 22:397–403.
Andrey A. Toropov and Alla P. Toropova. Improved Model for Biodegradability of Organic Compounds: The Correlation Contributions of Rings.
Chapter 8, In Book: Toxicity and Biodegradation Testing, Methods in Pharmacology and Toxicology. Edited: Ederio Dino Bidoia and Renato Nallin Montagnolli, Springer Science+Business Media LLC 2018. DOI 10.1007/978-1-4939-7425-2_8
Andrey A. Toropov, Ramon Carbó-Dorca, Alla P. Toropova, Index of Ideality of Correlation: new possibilities to validate QSAR: a case study. Structural Chemistry, (2018) 29: 33–38.
Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, Mario Salmona, Mutagenicity, Anticancer activity, and Blood brain barrier: Similarity and dissimilarity of molecular alerts. Toxicology Mechanisms and Methods, 28(5), 2018, 321-327.
Alla P. Toropova and Andrey A. Toropov, CORAL: QSAR Models for Carcinogenicity of Organic Compounds for Male and Female Rats. Computational Biology and Chemistry, Volume 72, February 2018, Pages 26–32.
A. Worachartcheewan, A. P. Toropova, A.A. Toropov, S. Siriwong, J. Prapojanasomboon, V. Prachayasittikul, C. Nanatasenamat,
Quantitative Structure-activity Relationship Study of Betulinic Acid Derivatives Against HIV using SMILES-based Descriptors.
Current Computer-Aided Drug Design, 2018;14(2):152-159.
Andrey A. Toropov, Alla P. Toropova, Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes. Chemical Physics Letters, 701 (2018) 137-146.
Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, and Emilio Benfenati,
Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results.
Chapter 27, In book: Orazio Nicolotti (ed.), Computational Toxicology: Methods and Protocols, Methods in Molecular Biology, vol. 1800,
https://doi.org/10.1007/978-1-4939-7899-1_27, © Springer Science+Business Media, LLC, part of Springer Nature 2018.
A.A. Toropov, A.P. Toropova, E. Benfenati, L. Diomede, M. Salmona, Use of Quasi-SMILES to model biological activity of "micelle-polymer" samples. Structural Chemistry (2018) 29: 1213–1223.
Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, Jean Lou Dorne, SAR for gastro-intestinal absorption and blood-brain barrier permeation of pesticides. Chemico-Biological Interactions, 290 (2018) 1–5.
Andrey A. Toropov, Alla P. Toropova, Giuseppa Raitano, Emilio Benfenati,CORAL: building up QSAR models for the chromosome aberration test.
Saudi Journal of Biological Sciences, 26 (2019) 1101-1106. https://doi.org/10.1016/j.sjbs.2018.05.013
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Danuta Leszczynska, Jerzy Leszczynski, Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES. BioSystems,169-170 (2018) 5-12.
Alla P. Toropova, Andrey A. Toropov. The index of ideality of correlation: Improvement of models for toxicity to algae. Natural Product Research, 33(15), 2019, 2200-2207.
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Sara Castiglioni,Renzo Bagnati, Alice Passoni, Ettore Zuccato, Roberto Fanelli. Quasi-SMILES as a tool to predict removal rates of pharmaceuticals and dyes in sewage. Process Safety and Environmental Protection, 118 (2018) 227-233.
Andrey A. Toropov, Alla P. Toropova, Use of The Index of Ideality of Correlation to improve predictive potential for biochemical endpoints. Toxicology Mechanisms and Methods, 2019, 29(1), 43–52.
Alla P. Toropova, Andrey A. Toropov, Aleksandar M. Veselinović, Jovana B. Veselinović, Danuta Leszczynska, Jerzy Leszczynski, Semi-correlations combined with the index of ideality of correlation: A tool to build up model of mutagenic potential. Molecular and Cellular Biochemistry, 2019, Volume 452, Issue 1–2, pp. 133–140.
Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski, The Index of Ideality of Correlation: Hierarchy of Monte Carlo models for Glass Transition Temperatures of Polymers. Journal of Polymer Research, (2018) 25:221-227.
M.Marzo, G.J. Lavado, F. Como, A.A. Toropov, A.P. Toropova, D. Baderna, C. Cappelli, A. Lombardo, C. Toma, M. Blázquez Sánchez, E. Benfenati,
QSAR models for Biocides. The example of the prediction of Daphnia Magna acute toxicity.
SAR and QSAR in Environmental Research, 31:3, 227-243, 2020. https://doi.org/10.1080/1062936X.2019.1709221
Claudia Ileana Cappelli, Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati,
Ecosystem ecology: models for acute toxicity of pesticides towards Daphnia magna.
Environmental Toxicology and Pharmacology 80 (2020) 103459.https://doi.org/10.1016/j.etap.2020.103459
M. Marzo, G.J. Lavado, F. Como, A.A. Toropov, A.P. Toropova, D. Baderna, C. Cappelli, A. Lombardo, C. Toma, M. Blázquez Sánchez, E. Benfenati,
QSAR models for Biocides. The example of the prediction of Daphnia Magna acute toxicity.
SAR and QSAR in Environmental Research, 31:3, 227-243, 2020.
Alla P. Toropova, Andrey A. Toropov, Edoardo Carnesecchi, Emilio Benfenati, Jean Lou Dorne,
The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides.
Environmental Science and Pollution Research (2020) 27: 13339-13347.
Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati,
QSAR model for pesticides toxicity to Rainbow Trout based on "ideal correlations".
Aquatic Toxicology 227 (2020) 105589.
Claudia Ileana Cappelli, Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati,
Ecosystem ecology: models for acute toxicity of pesticides towards Daphnia magna.
Environmental Toxicology and Pharmacology, 80 (2020) 103459.
A.A. Toropov, A.P. Toropova, G. Selvestrel, D. Baderna, E. Benfenati,
Prediction of No Observed Adverse Effect Concentration for Inhalation toxicity: Monte Carlo approach.
SAR and QSAR in Environmental Research, 31(12), 2020, 1-12. DOI: 10.1080/1062936X.2020.1841827
J.L.C.M. Dorne, J. Richardson, A. Livaniou, E. Carnesecchi, L. Ceriani, R. Baldin, S. Kovarich, M. Pavan, E. Saouter, F. Biganzoli, L. Pasinato, M. Zare Jeddi, T. P. Robinson, G.E.N. Kass, A.K.D. Liem, A.A. Toropov, A.P. Toropova, C. Yang, A. Tarkhov, N. Georgiadis, M.R. Di Nicola, A. Mostrag, H. Verhagen, A. Roncaglioni, E. Benfenati, A. Bassan. EFSA’s OpenFoodTox: An open source toxicological database on chemicals
in food and feed and its future developments. Environment International 146 (2021) 106293. https://doi.org/10.1016/j.envint.2020.106293
Benfenati, Emilio, Roncaglioni, Alessandra, Carnesecchi, Edoardo, Mazzucotelli, Matilda, Marzo, Marco, Toropov, Andrey, Toropova, Alla, Baldin, Rossella, Ciacci, Andrea, Kovarich, Simona, Sartori, Luca, Yang, Chihae, Magdziarz, Tomasz, Hobocienski, Bryan, Mostrag, Aleksandra, 2021. Maintenance, update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0.
EFSA supporting publication 2021: 18( 3):EN-6476. 46pp. doi:10.2903/sp.efsa.2021.EN-6476
Giovanna J. Lavado, Diego Baderna, Edoardo Carnesecchi, Alla P. Toropova, Andrey A. Toropov, Jean Lou CM Dorne, Emilio Benfenati,
QSAR models for soil ecotoxicity: development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida,
Journal of Hazardous Materials 423 (2022) 127236, https://doi.org/10.1016/j.jhazmat.2021.127236.
Andrey A. Toropov, Matteo R. Di Nicola, Alla P. Toropova, Alessandra Roncaglioni, Edoardo Carnesecchi, Nynke I. Kramer, Antony J. Williams, Manuel E. Ortiz-Santaliestra, Emilio Benfenati, Jean-Lou C.M. Dorne,
A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians,
Science of the Total Environment 830 (2022) 154795. https://doi.org/10.1016/j.scitotenv.2022.154795
Toropov, A.A., Di Nicola, M.R., Toropova, A.P., Roncaglioni, A., Dorne, J.L.C.M., Benfenati, E. Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles. Chemosphere 312 (2023) 137224.
https://doi.org/10.1016/j.chemosphere.2022.137224
A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati, Binding organophosphate pesticides to acetylcholinesterase: Risk assessment using the Monte Carlo method.
Toxicological & Environmental Chemistry, Accepted Feb 9, 2023. DOI: 10.1080/02772248.2023.2181348
A.A. Toropov, A.P. Toropova, E. Benfenati, The Index of Ideality of Correlation: QSAR model of acute toxicity
for zebrafish (Danio rerio) embryo. International Journal of Environmental Research (2019) 13: 387–394.
Andrey A. Toropov, Alla P. Toropova, The Monte Carlo Method as a tool to build up predictive QSPR/QSAR. Curr. Comput. Aided Drug Des. 2020, 16(3), 197 – 206. DOI: 10.2174/1573409915666190328123112
Andrey A. Toropov, Alla P. Toropova, Danuta Leszczynska, Jerzy Leszczynski, "Ideal correlations" for biological activity of peptides. BioSystems, 181 (2019) 51-57. https://doi.org/10.1016/j.biosystems.2019.04.008
Andrey A. Toropov, Alla P. Toropova, Gianluca Selvestrel, Emilio Benfenati, Idealization of correlations between optimal SMILES-based descriptors and skin sensitization. SAR and QSAR in Environmental Research, 30(6), 2019, 447-455.
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, QSPR as a random event: solubility of fullerenes C[60] and C[70]. Fullerenes, Nanotubes and Carbon Nanostructures, 2019, 27(10), 816-821. DOI: 10.1080/1536383X.2019.1649659
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, QSAR-models, validation, and IIC-paradox for drug toxicity.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 5(1), 2020, 22-43. DOI: 10.4018/IJQSPR.2020010102
E. Carnesecchi, A.A. Toropov, A.P. Toropova, N. Kramer, C.Svendsen, J.L. Dorne, E. Benfenati, Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models.
Science of The Total Environment, 704 (2020) 135302.
Andrey A.Toropov, Alla P.Toropova, Marco Marzo, Emilio Benfenati, Use of the index of ideality of correlation to improve aquatic solubility model. Journal of Molecular Graphics and Modelling 96 (2020) 107525.
Alla P. Toropova, Pablo R. Duchowicz, Laura M. Saavedra, Eduardo A. Castro and Andrey A. Toropov, The use of the index of ideality of correlation to build up models for bioconcentration factor. Molecular Informatics, 2020, 39, 1900070. https://doi.org/10.1002/minf.201900070
Alla P. Toropova, Andrey A. Toropov, Edoardo Carnesecchi,
Emilio Benfenati, Jean Lou Dorne,The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides.
Environmental Science and Pollution Research, 27, pages 13339–13347 (2020).DOI: 10.1007/s11356-020-07820-6
Andrey A. Toropov, Alla P. Toropova, QSPR/QSAR: state-of-art, weirdness, the future. Molecules 2020, 25(6), 1292. https://doi.org/10.3390/molecules25061292
G.J. Lavado, D. Gadaleta, C. Toma, A. Golbamaki, A.A. Toropov, A.P. Toropova, M.
Marzo, D. Baderna, E. Benfenati, Zebrafish AC50 Modelling: (Q)SAR Models to Predict
Developmental Toxicity in Zebrafish Embryo. Ecotoxicology and Environmental Safety
202 (2020) 110936.
Andrey A. Toropov and Alla P. Toropova,
Correlation Intensity Index: building up models for mutagenicity of silver nanoparticles.
Science of the Total Environment 737 (2020) 139720. https://doi.org/10.1016/j.scitotenv.2020.139720
K. Jafari, M.H. Fatemi, A.P. Toropova, A.A. Toropov,
Correlation Intensity Index (CII) as a criterion of predictive potential: applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids.
Chemical Physics Letters 754 (2020) 137614. DOI: 10.1016/j.cplett.2020.137614
Alla P. Toropova and Andrey A. Toropov,
Fullerenes C60 and C70: a model for solubility by applying the correlation intensity index.
Fullerenes, Nanotubes and Carbon Nanostructures, 2020; 28:11, 900-906. DOI:10.1080/1536383X.2020.1779705
Shahin Ahmadi, Alla P. Toropova, and Andrey A. Toropov,
Correlation Intensity Index: Mathematical modelling of cytotoxicity of metal oxide nanoparticles.
Nanotoxicology, 2020, 14:8, 1118-1126. DOI: 10.1080/17435390.2020.1808252
Andrey A. Toropov, Natalia Sizochenko, Alla P. Toropova, Danuta Leszczynska, Jerzy Leszczynski,
Advancement of predictive modeling of zeta potentials in metal oxide nanoparticles with correlation intensity index (CII).
Journal of Molecular Liquids, 317 (2020) 113929.
https://doi.org/10.1016/j.molliq.2020.113929
Alla P. Toropova, Maria Raškova, Ivan Raška Jr., Andrey A. Toropov,
The sequence of amino acids as the basis for the model of biological activity of peptides.
Theoretical Chemistry Accounts, 140, 15 (2021). DOI:10.1007/s00214-020-02707-8
A.A. Toropov, A.P. Toropova, M. Marzo, E. Carnesecchi, G. Selvestrel, E. Benfenati,
Pesticides, Cosmetics, Drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity.
Molecular Diversity, 25, 1137–1144 (2021). DOI: 10.1007/s11030-020-10085-3
Andrey A. Toropov and Alla P. Toropova,
The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: a pun or a reliable law?
Toxicology Letters, 340 (2021) 133-140. https://doi.org/10.1016/j.toxlet.2021.01.015
A.P. Toropova, A.A. Toropov, A. Lombardo, G. Lavado, and E. Benfenati,
Paradox of "ideal correlations": improved model for air half-life of persistent organic pollutants.
Environmental Technology 2022, Vol. 43, No. 16, 2510-2515. DOI: 10.1080/09593330.2021.1882588
Toropova, A.P., Toropov, A.A. Can the Monte Carlo method predict the toxicity of binary mixtures? Environ Sci Pollut Res (2021) 28: 39493–39500. https://doi.org/10.1007/s11356-021-13460-1
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati, The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella).
Computers in Biology and Medicine, 133 (2021) 104370. https://doi.org/10.1016/j.compbiomed.2021.104370
A.P. Toropova and A.A. Toropov, The system of self-consistent of models: a new approach to build up and validation of predictive models of the octanol/water partition coefficient for gold nanoparticles.
Int. J. Environ. Res. 15(4), 2021, 709-722. DOI: 10.1007/s41742-021-00346-w
Andrey A. Toropov, Alla P. Toropova,
The system of self-consistent models for the uptake of nanoparticles in PaCa2 cancer cells.
Nanotoxicology, 15:7, 2021, 995-1004. DOI:10.1080/17435390.2021.1951387
Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method.
SAR and QSAR in Environmental Research, 32(9), (2021) 689-698. DOI:10.1080/1062936X.2021.1952649
A.P. Toropova, A.A. Toropov, D. Leszczynska, J. Leszczynski,
Application of quasi-SMILES to the model of gold-nanoparticles uptake in A549 cells.
Computers in Biology and Medicine 136 (2021) 104720. https://doi.org/10.1016/j.compbiomed.2021.104720
Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, Emilio Benfenati
The system of self-consistent semi-correlations as one of the tools of cheminformatics for design antiviral drugs.
New Journal of Chemistry, 2021, 45, 20713 - 20720. https://doi.org/10.1039/D1NJ03394H
Andrey A. Toropov, Alla P. Toropova, Valentin O. Kudyshkin,
The system of self-consistent QSPR-models for refractive index of polymers.
Structural Chemistry,2022 ; 33 : 617-624.
https://doi.org/10.1007/s11224-021-01875-y.
Andrey A. Toropov, Alla P. Toropova, Aleksandar Veselinović, Danuta Leszczynska, Jerzy Leszczynski,
SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model.
Journal of Biomolecular Structure and Dynamics, 2022, 40(2), 780-786. DOI: 10.1080/07391102.2020.1818627
Andrey A. Toropov, Matteo R. Di Nicola, Alla P. Toropova, Alessandra Roncaglioni, Edoardo Carnesecchi, Nynke I. Kramer, Antony J. Williams, Manuel E. Ortiz-Santaliestra, Emilio Benfenati, Jean-Lou C.M. Dorne,
A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians,
Science of the Total Environment 830 (2022) 154795. https://doi.org/10.1016/j.scitotenv.2022.154795
Alla P. Toropova and Andrey A. Toropov, Quasi-SMILES as a basis to build up models of endpoints for nanomaterials. Environmental Technology, 44(28), 2023, 4460-4467.
DOI: 10.1080/09593330.2022.2093655
Toropov, A.A., Di Nicola, M.R., Toropova, A.P., Roncaglioni, A., Dorne, J.L.C.M., Benfenati, E. Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles. Chemosphere 312 (2023) 137224.
https://doi.org/10.1016/j.chemosphere.2022.137224
Alla P. Toropova, Andrey A. Toropov, Use of the Index of Ideality of Correlation to improve models of Eco-toxicity. Environmental Science and Pollution Research, (2018) 25: 31771-31775.
A.P. Toropova, A.A. Toropov, E. Benfenati, D. Leszczynska, J. Leszczynski, Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique. Anti-Cancer Agents in Medicinal Chemistry, 19(2), 2019, 148 – 153.
Alla P. Toropova, Andrey A. Toropov, Quasi-SMILES: Quantitative Structure - Activity Relationships to predict anti-cancer activity. Molecular Diversity (2019) 23: 403–412.
Andrey A. Toropov and Alla P. Toropova, Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation. Anticancer Research, 38: 6189-6194 (2018).
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Semi-correlations as a tool to build up categorical (active/inactive) model of GABAA receptor modulators activity.
Struct. Chem. (2019) 30 (3): 853–861.
Alla Toropova; Andrey Toropov; Emilio Benfenati,
Idealized correlations: prediction of solubility of fullerene in organic solvents ,
Published: 10 December 2018 by MDPI AG in MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition
session WCUCW-02: West Coast University Capstone Workshop, WCU, Miami, USA, 2018 ( doi: 10.3390/mol2net-04-05898 )
Andrey A. Toropov, Ivan Raška Jr., Alla P. Toropova, Maria Raškova, Aleksandar M. Veselinović, Jovana B. Veselinović, The study of the Index of Ideality of Correlation
As a new criterion of predictive potential of QSPR/QSAR-models. Science of the Total Environment 659 (2019) 1387–1394.
Alla P. Toropova, Andrey A. Toropov, QSPR and nano-QSPR: what is the difference? Journal of Molecular Structure, 1182 (2019) 141-149.
Alla P. Toropova, Andrey A. Toropov, Does the index of ideality of correlation detect the better model correctly? Molecular Informatics, 2019, 38, 1800157. https://doi.org/10.1002/minf.201800157
Alla P. Toropova and Andrey A. Toropov, Applying of the Monte Carlo method for the prediction of behavior of peptides, Current Protein & Peptide Science (2019) 20(12): 1151 - 1157.
P.G.R. Achary, A.P. Toropova, A.A. Toropov, Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness. Food Research International 122 (2019) 40–46.
Alla P. Toropova and Andrey A. Toropov, Whether the Validation of the Predictive Potential of Toxicity Models is Solved Task? Current Topics in Medicinal Chemistry, 2019; 19(29): 2643 - 2657.
Alla P. Toropova, Andrey A. Toropov, Edoardo Carnesecchi, Emilio Benfenati, Jean Lou Dorne. The index of ideality of correlation: models for flammability of binary liquid mixtures. Chemical Papers, 2020, 74(2): 601-609. https://doi.org/10.1007/s11696-019-00903-w
Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski, The index of ideality of correlation: models of flash points of ternary mixtures. New Journal of Chemistry, 2020, 44, 4858 - 4868. DOI: 10.1039/D0NJ00121J
Andrey A. Toropov, Alla P. Toropova, Edoardo Carnesecchi, Emilio Benfenati, Jean Lou Dorne, The Index of Ideality of Correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity. Structural Chemistry, (2020) 31: 1441–1448.DOI: 10.1007/s11224-020-01525-9
A. A. Toropov, A. P. Toropova, V. O. Kudyshkin, N. I. Bozorov, S. Sh. Rashidova,
Applying of the Monte Carlo technique to build up models of glass transition temperatures of diverse polymers.
Structural Chemistry, (2020) 31: 1739-1743. DOI: 10.1007/s11224-020-01588-8
A.A. Toropov, A.P. Toropova, E. Benfenati,
“Ideal correlations” for the predictive toxicity to Tetrahymena pyriformis.
Toxicology Mechanisms and Methods, 30(8), 2020, 605-610. DOI: 10.1080/15376516.2020.1801928
Andrey A.Toropov, Alla P.Toropova, Emilio Benfenati, QSAR model for pesticides toxicity to Rainbow Trout based on “ideal correlations”.
Aquatic Toxicology 227 (2020) 105589. https://doi.org/10.1016/j.aquatox.2020.105589
A.P. Toropova, A.A. Toropov, D. Leszczynska, J. Leszczynski, How the CORAL software can be used to select compounds for treatment of neurodegenerative diseases?
Toxicology and Applied Pharmacology 408 (2020) 115276. https://doi.org/10.1016/j.taap.2020.115276
A.A. Toropov, A.P. Toropova, G. Selvestrel, D. Baderna, E. Benfenati,
Prediction of No Observed Adverse Effect Concentration for Inhalation toxicity: Monte Carlo approach.
SAR and QSAR in Environmental Research, 31(12), 2020, 1-12. DOI: 10.1080/1062936X.2020.1841827
P.G.R. Achary, A. P. Toropova, A.A. Toropov,
Prediction of the self-accelerating decomposition temperature of organic peroxides.
Process Safety Progress, 2021; 40: e12189. DOI: 10.1002/prs.12189
A. Worachartcheewan, A. P. Toropova, A. A. Toropov, R. Pratiwi, V. Prachayasittikul, C. Nantasenamat,
Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2.
Combinatorial Chemistry & High Throughput Screening, 24 (8), 2021, 1217 - 1228. DOI: 10.2174/1386207323666200902141907
Andrey A. Toropov and Alla P. Toropova,
Quasi-SMILES as a basis for the development of models for the toxicity
of ZnO nanoparticles.
Science of the Total Environment, 772 (2021) 145532. https://doi.org/10.1016/j.scitotenv.2021.145532
A.P. Toropova, A.A. Toropov, E. Benfenati, The self-organizing vector of atom-pairs proportions: use to develop models for melting points.
Structural Chemistry (2021) 32: 967–971. https://doi.org/10.1007/s11224-021-01778-y
A.A. Toropov, A.P. Toropova, A. Lombardo, A. Roncaglioni, G. Lavado, E. Benfenati,
The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds.
SAR and QSAR in Environmental Research, 2021, 32:6, 463-471. DOI: 10.1080/1062936X.2021.1914156
Alla P. Toropova, Andrey A. Toropov, Jerzy Leszczynski, Natalia Sizochenko,
Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions.
Environmental Toxicology and Pharmacology 86 (2021) 103665.DOI: 10.1016/j.etap.2021.103665
Giovanna J. Lavado, Diego Baderna, Edoardo Carnesecchi, Alla P. Toropova, Andrey A. Toropov, Jean Lou CM Dorne, Emilio Benfenati,
QSAR models for soil ecotoxicity: development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida,
Journal of Hazardous Materials 423 (2022) 127236, https://doi.org/10.1016/j.jhazmat.2021.127236
A. P. Toropova, A. A. Toropov, E. Benfenati, Semi-correlations as a tool to model for skin sensitization. Food and Chemical Toxicology, 157 (2021) 112580. https://doi.org/10.1016/j.fct.2021.112580
A.P. Toropova, A.A. Toropov, A. Roncaglioni, and E. Benfenati,
The system of self-consistent models of vapour pressure.
Chemical Physics Letters, 790 (2022) 139354.
https://doi.org/10.1016/j.cplett.2022.139354
Kimia Jafari, Mohammad Hossein Fatemi, Alla P. Toropova, Andrey A. Toropov,
The development of nano-QSPR models for viscosity of nanofluids using the index of ideality of correlation and the correlation intensity index,
Chemometrics and Intelligent Laboratory Systems 222 (2022) 104500. https://doi.org/10.1016/j.chemolab.2022.104500
N. Fjodorova, M. Novič, K. Venko, V. Drgan, B. Rasulev, M. Türker Saçan, S.S. Erdem, G. Tugcu, A.P. Toropova, A.A. Toropov, How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases.
Computational and Structural Biotechnology Journal, 20 (2022) 913–924. https://doi.org/10.1016/j.csbj.2022.02.006
Andrey A. Toropov, Alla P. Toropova, P. Ganga Raju Achary,
Maria Raškova, Ivan Raška Jr. The searching for agents for Alzheimer's disease treatment via the system of self-consistent models.
Toxicology Mechanisms and Methods, 32:7, (2022) 549-557. https://doi.org/10.1080/15376516.2022.2053918
A.P. Toropova, A.A. Toropov, E.L. Viganò, E. Colombo, A. Roncaglioni, E. Benfenati,
Carcinogenicity Prediction Using the Index of Ideality of Correlation.
SAR and QSAR in Environmental Research, 2022; 33(6), 419-428, DOI:10.1080/1062936X.2022.2076736
A.A. Toropov, F. Kjeldsen, A.P. Toropova, Use of quasi-SMILES to build models based on quantitative results from
experiments with nanomaterials. Chemosphere 303 (2022) 135086. https://doi.org/10.1016/j.chemosphere.2022.135086
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati,
Monte Carlo technique to study of the adsorption affinity of azo dyes with applying new statistical criteria of the predictive potential.
SAR and QSAR in Environmental Research, 33:8, 2022, 621-630. DOI: 10.1080/1062936X.2022.2104369
Alla P. Toropova, Andrey A. Toropov, Natalja Fjodorova, Quasi-SMILES for predicting toxicity of Nano-mixtures to Daphnia Magna.
NanoImpact 28 (2022) 100427. https://doi.org/10.1016/j.impact.2022.100427
A.P. Toropova, J. Meneses, E. Alfaro-Moreno, A.A. Toropov,
The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of Nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions
Drug and Chemical Toxicology, Accepted Oct 12, 2022. https://doi.org/10.1080/01480545.2023.2174986
Toropova, A.P.; Toropov, A.A.; Fjodorova, N. In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in
THP-1 cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions
Represented by Means of Quasi-SMILES. Int. J. Mol. Sci. 2023, 24, 2058. https://doi.org/10.3390/ijms24032058
A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati, Binding organophosphate pesticides to acetylcholinesterase: Risk assessment using the Monte Carlo method. Toxicological & Environmental Chemistry, 2023, 105(1-7), 19-27.
DOI: 10.1080/02772248.2023.2181348
A.A. Toropov, A.P. Toropova, A. Roncaglioni, E. Benfenati, The system of self-consistent models for pesticide toxicity to Daphnia Magna, Toxicology Mechanisms and Methods,
2023, 33:7, 578-583, DOI: 10.1080/15376516.2023.2197487
A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati, D. Leszczynska, J. Leszczynski,
CORAL: Model of ecological impact of heavy metals on soils via the study of modification of concentration of biomolecules in Earthworms (Eisenia fetida).
Archives of Environmental Contamination and Toxicology, (2023) 84:504-515. https://doi.org/10.1007/s00244-023-01001-5
A.A. Toropov, A.P. Toropova, A. Roncaglioni, E. Benfenati, Does the accounting of the local symmetry fragments in SMILES improve the predictive potential of the QSPR-model for Henry's law constants?
Environmental Science: Advances, 2023, 2, 916 - 921. https://doi.org/10.1039/D3VA00012E
A.A. Toropov, A.P. Toropova, P.G.R. Achary,
Prediction of n-octanol-water partition coefficient of platinum (IV) complexes using correlation weights of fragments of local symmetry.
Structural Chemistry, 34, 1517-1526 (2023). https://doi.org/10.1007/s11224-023-02197-x
A.A. Toropova, A.P. Toropova, D. Leszczynska, J. Leszczynski,
Development of self-consistency models of anticancer activity of nanoparticles that were observed under different experimental conditions using quasi-SMILES.
Nanomaterials, 2023, 13(12), 1852. https://doi.org/10.3390/nano13121852
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati,
The enhancement scheme for the predictive ability of QSAR: a case of mutagenicity.
Toxicology in Vitro, 91, 2023, 105629. https://doi.org/10.1016/j.tiv.2023.105629
Alla P. Toropova, Andrey A. Toropov,
Nanomaterials: quasi-SMILES as a flexible basis for regulation and environmental risk assessment.
Science of the Total Environment 823 (2022) 153747. https://doi.org/10.1016/j.scitotenv.2022.153747
G. Selvestrel, G.J. Lavado, A.P. Toropova, A.A. Toropov, D. Gadaleta, M. Marzo, D. Baderna, E. Benfenati,
Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity. International Journal of Molecular Sciences, 2022, 23, 6615. https://doi.org/10.3390/ijms23126615
Andrey A. Toropov, Devon Barnes, Alla P. Toropova, Alessandra Roncaglioni, Alasdair R. Irvine, Rosalinde Masereeuw, Emilio Benfenati, CORAL models for drug induced nephrotoxicity. Toxics, 2023, 11, 293. https://doi.org/10.3390/toxics11040293
A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati,
The system of self-consistent models: QSAR analysis of drug-induced liver toxicity. Toxics, 2023; 11(5): 419. https://doi.org/10.3390/toxics11050419
A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati, D. Leszczynska, J. Leszczynski,
The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds.
Journal of Molecular Modeling, 29 (2023) 218. https://doi.org/10.1007/s00894-023-05632-2
Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, Emilio Benfenati,
In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry,
Mutation Research - Genetic Toxicology and Environmental Mutagenesis 891 (2023) 503684,
https://doi.org/10.1016/j.mrgentox.2023.503684.
Toropova, A.P.; Toropov, A.A.; Fjodorova, N.
QSPR and Nano-QSPR: Which One Is Common? The Case of Fullerenes Solubility.
Inorganics 2023, 11, 344. https://doi.org/10.3390/inorganics11080344
Alla P. Toropova and Andrey A. Toropov,
Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity.
Amino Acids, (2023) 55:1437-1445.
DOI: 10.1007/s00726-023-03322-0
Alla P. Toropova, Andrey A. Toropov, Ivan Raska Jr., Maria Raskova, Ramon Carbό-Dorca,
The prediction of retention time of pesticide based on the Monte Carlo method with use the vector of ideality of correlation and correlation weights of local symmetry fragments.
Journal of Mathematical Chemistry, Accepted September 4, 2023. https://doi.org/10.1007/s10910-023-01517-0
N. Fjodorova, M. Novic, K. Venko, B. Rasulev, M.T. Saçan, G. Tugcu, S.S. Erdem, A.P. Toropova, A.A. Toropov,
Cheminformatic and Machine Learning Approaches to the Assessment of Aquatic Toxicity Profile of Fullerene Derivatives.
Int. J. Mol. Sci. 2023, 24, 14160. https://doi.org/10.3390/ijms241814160
Toropov, A.A.; Toropova, A.P.; Roncaglioni, A.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. The System of Self-Consistent Models: The Case of Henry’s Law Constants.
Molecules 2023, 28, 7231. https://doi.org/10.3390/molecules28207231
Toropova, A. P., Toropov, A. A., Roncaglioni, A., & Benfenati, E. Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity towards tadpoles?
Toxicology Mechanisms and Methods,(2024), 1–9. https://doi.org/10.1080/15376516.2024.2332617
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, and Emilio Benfenati,
Using the Correlation Intensity Index to build a model of cardiotoxicity.
Molecules 2023, 28, 6587. https://doi.org/10.3390/molecules28186587
and PreNanoTox
have been aimed to intensive studies of nanomaterials and their properties.
(Journal of Theoretical Biology 416 (2017) 113–118).
orcid.org/0000-0001-6864-6340