References
- O.W. Brawley, The role of government and regulation in cancer prevention, Lancet Oncol. 18 (2017), pp. e483–e493. doi:https://doi.org/10.1016/S1470-2045(17)30374-1.
- European Commission, Joint Research Centre, Worth, A., Corvi, R., Madia, F., Analysis of carcinogenicity testing for regulatory purposes in the European Union: review of the current demand of in vivo carcinogenicity studies across sectors, Publications Office, 2016, https://data.europa.eu/doi/10.2788/547846 .
- W.-X. Wan, Y. Chen, J. Zhang, F. Shen, L. Luo, S.-H. Deng, H. Xiao, W. Zhou, O.-P. Deng, H. Yang, Y.-L. Xiao, C.-R. Huang, D. Tian, J.-S. He, and Y.-J. Wang, Mechanism-basedstructure-activity relationship (SAR) analysis of aromatic amines and nitroaromatics carcinogenicity via statistical analyses based on CPDB, Toxicol. In Vitro 58 (2019), pp. 13–25. doi:https://doi.org/10.1016/j.tiv.2019.03.017.
- C. Bossa, R. Benigni, O. Tcheremenskaia, and C.L. Battistelli, (Q)SAR methods for predicting genotoxicity and carcinogenicity: Scientific rationale and regulatory frameworks, Meth. Mol. Biol. 1800 (2018), pp. 447–473.
- N. Fjodorova and M. Novič, Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity, Comput. Struct. Biotechnol. J. 1 (2012), pp. e201207003. doi:https://doi.org/10.5936/csbj.201207003.
- C. Toma, A. Manganaro, G. Raitano, M. Marzo, D. Gadaleta, D. Baderna, A. Roncaglioni, N. Kramer, and E. Benfenati, QSAR models for human carcinogenicity: An assessment based on oral and inhalation slope factors, Molecules 26 (2020), pp. 127. doi:https://doi.org/10.3390/molecules26010127.
- S. Manganelli and E. Benfenati, Use of read-across tools, Meth. Mol. Biol. 1425 (2016), pp. 305–322.
- A.A. Toropov and A.P. Toropova, The system of self-consistent models for the uptake of nanoparticles in PaCa2 cancer cells, Nanotoxicology 15 (2021), pp. 995–1004.
- S. Ahmadi, Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria, Chemosphere 242 (2020), pp. 125192. doi:https://doi.org/10.1016/j.chemosphere.2019.125192.
- A.P. Toropova and A.A. Toropov, Quasi-SMILES: Quantitative structure–activity relationships to predict anticancer activity, Mol. Divers. 23 (2019), pp. 403–412. doi:https://doi.org/10.1007/s11030-018-9881-9.
- A.P. Toropova, A.A. Toropov, E. Benfenati, D. Leszczynska, and J. Leszczynski, Virtual screening of anti-cancer compounds: Application of Monte Carlo technique, Anti-Cancer Agents Med. Chem. 19 (2019), pp. 148–153. doi:https://doi.org/10.2174/1871520618666181025122318.
- A.A. Toropov, A.P. Toropova, E. Benfenati, and M. Salmona, Mutagenicity, anticancer activity and blood brain barrier: Similarity and dissimilarity of molecular alerts, Toxicol. Mech. Meth. 28 (2018), pp. 321–327. doi:https://doi.org/10.1080/15376516.2017.1422579.
- E. Pontiki, D. Hadjipavlou-Litina, G. Geromichalos, and A. Papageorgiou, Anticancer activity and quantitative-structure activity relationship (QSAR) studies of a series of antioxidant/anti-inflammatory aryl-acetic and hydroxamic acids, Chem. Biol. Drug. Des. 74 (2009), pp. 266–275. doi:https://doi.org/10.1111/j.1747-0285.2009.00864.x.
- M. Das, B. Das, and A. Samanta, Antioxidant and anticancer activity of synthesized 4-amino-5-((aryl substituted)-4H-1,2,4-triazole-3-yl)thio-linked hydroxamic acid derivatives, J. Pharm. Pharmacol. 71 (2019), pp. 1400–1411. doi:https://doi.org/10.1111/jphp.13131.
- A.A. El-Helby, H. Sakr, R.R.A. Ayyad, K. El-Adl, M.M. Ali, and F. Khedr, Design, synthesis, in vitro anti-cancer activity, ADMET profile and molecular docking of novel triazolo[3,4-a]phthalazine derivatives targeting VEGFR-2 enzyme, Anticancer Agents Med. Chem. 18 (2018), pp. 1184–1196. doi:https://doi.org/10.2174/1871520618666180412123833.
- A.B. Umar, A. Uzairu, G.A. Shallangwa, and S. Uba, QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2, Heliyon 6 (2020), pp. e03640. doi:https://doi.org/10.1016/j.heliyon.2020.e03640.
- A.A. Toropov and A.P. Toropova, Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation, Anticancer Res. 38 (2018), pp. 6189–6194. doi:https://doi.org/10.21873/anticanres.12972.
- A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski, Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents, Anticancer Agents Med. Chem. 12 (2012), pp. 807–817. doi:https://doi.org/10.2174/187152012802650255.
- A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski, SMILES-based QSAR approaches for carcinogenicity and anticancer activity: Comparison of correlation weights for identical SMILES attributes, Anticancer Agents Med. Chem. 11 (2011), pp. 974–982. doi:https://doi.org/10.2174/187152011797927625.
- E. Benfenati, A.A. Toropov, A.P. Toropova, A. Manganaro, and R. Gonella Diaza, CORAL software: QSAR for anticancer Agents, Chem. Biol. Drug Des. 77 (2011), pp. 471–476. doi:https://doi.org/10.1111/j.1747-0285.2011.01117.x.
- A.P. Toropova and A.A. Toropov, CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats, Comput. Biol. Chem. 72 (2018), pp. 26–32. doi:https://doi.org/10.1016/j.compbiolchem.2017.12.012.
- A.P. Toropova and A.A. Toropov, CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method, Eur. J. Pharm. Sci. 52 (2014), pp. 21–25. doi:https://doi.org/10.1016/j.ejps.2013.10.005.
- N. Li, J. Qi, P. Wang, X. Zhang, T. Zhang, and H. Li, Quantitative structure–activity relationship (QSAR) study of carcinogenicity of polycyclic aromatic hydrocarbons (PAHs) in atmospheric particulate matter by random forest (RF), Anal. Meth. 11 (2019), pp. 1759–9660.
- P. Duchowicz, N. C. Comelli, E. V. Ortiz, and E. A. Castro, QSAR study for carcinogenicity in a large set of organic compounds, Curr. Drug Saf. 7 (2012), pp. 282–288. doi:https://doi.org/10.2174/157488612804096623.
- N. Fjodorova and M. Novič, Rodent carcinogenicity dataset, Dataset Papers in Science (2013), pp. 361615.
- A.P. Toropova, A.A. Toropov, R. Gonella Diaza, E. Benfenati, and G. Gini, Analysis of the co-evolutions of correlations as a tool for QSAR-modeling carcinogenicity: An unexpected good prediction based on a model that seems untrustworthy, Cent. Eur. J. Chem. 9 (2011), pp. 165–174.
- A.A. Toropov, A.P. Toropova, and E. Benfenati, SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes, Eur. J. Med. Chem. 45 (2010), pp. 3581–3587. doi:https://doi.org/10.1016/j.ejmech.2010.05.002.
- T. Li, W. Tong, R. Roberts, Z. Liu, and S. Thakkar, DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation, Front. Artif. Intell. 4 (2021), pp. 757780. doi:https://doi.org/10.3389/frai.2021.757780.
- R. Benigni, C. Bossa, A.M. Richard, and C. Yang, A novel approach: Chemical relational databases, and the role of the ISSCAN database on assessing chemical carcinogenicity, Ann. Ist. Super. Sanita 44 (2008), pp. 48–56.
- L.S. Gold, The Carcinogenic Potency Project and Database (CPDB). University of California, National Laboratory; National Library of Medicine‘s (NLM®), Berkeley; Lawrence Berkeley, 2011. https://files.toxplanet.com/cpdb/cpdb.html
- CAESAR project, no. 022674 – SSPI. Coordinator of the project: E. Benfenati, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Giuseppe La Masa, 19, 20156, Milan, Italy. http://www.caesar-project.eu/ (Accessed May 1, 2022).
- D. Kirkland, M. Aardema, L. Henderson, and L. Müller, Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens, Mutat. Res. Genet. Toxicol. Environ. Mutagen. 584 (2005), pp. 1–256. doi:https://doi.org/10.1016/j.mrgentox.2005.02.004.
- D. Weininger, SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules, J. Chem. Inf. Comput. Sci. 28 (1988), pp. 31–36. doi:https://doi.org/10.1021/ci00057a005.
- A.A. Toropov, A.P. Toropova, and E. Benfenati, Additive SMILES-based carcinogenicity models: Probabilistic principles in the search for robust predictions, Int. J. Mol. Sci. 10 (2009), pp. 3106–3127. doi:https://doi.org/10.3390/ijms10073106.
- A.P. Toropova and A.A. Toropov, The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Sci. Total Environ. 586 (2017), pp. 466–472. doi:https://doi.org/10.1016/j.scitotenv.2017.01.198.
- P. Kumar, A. Kumar, and J. Sindhu, Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR, SAR QSAR Environ. Res. 30 (2019), pp. 63–80. doi:https://doi.org/10.1080/1062936X.2018.1564067.
- M. Javidfar and S. Ahmadi, QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation, SAR QSAR Environ. Res. 31 (2020), pp. 717–739. doi:https://doi.org/10.1080/1062936X.2020.1806922.
- A.P. Toropova, A.A. Toropov, E. Carnesecchi, E. Benfenati, and J.L. Dorne, The index of ideality of correlation: Models for flammability of binary liquid mixtures, Chem. Pap. 74 (2020), pp. 601–609. doi:https://doi.org/10.1007/s11696-019-00903-w.
- S. Ahmadi and A. Akbari, Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method, SAR QSAR Environ. Res. 29 (2018), pp. 895–909. doi:https://doi.org/10.1080/1062936X.2018.1526821.
- P. Kumar and A. Kumar, In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation, SAR QSAR Environ. Res. 31 (2020), pp. 697–715. doi:https://doi.org/10.1080/1062936X.2020.1806105.
- P. Kumar, A. Kumar, and J. Sindhu, In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 525–541. doi:https://doi.org/10.1080/1062936X.2019.1629998.
- S. Ahmadi, S. Lotfi, and P. Kumar, Quantitative structure–toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation, Toxicol. Mech. Meth. 32 (2022), pp. 302–312. doi:https://doi.org/10.1080/15376516.2021.2000686.
- P. Kumar and A. Kumar, Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling, Nanotoxicology 15 (2021), pp. 1199–1214. doi:https://doi.org/10.1080/17435390.2021.2008039.
- S. Ahmadi, S. Lotfi, and P. Kumar, A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants, SAR QSAR Environ. Res. 31 (2020), pp. 935–950. doi:https://doi.org/10.1080/1062936X.2020.1842495.
- A. Kumar and P. Kumar, Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation, SAR QSAR Environ. Res. 32 (2021), pp. 817–834. doi:https://doi.org/10.1080/1062936X.2021.1973095.
- W. Bunmahotama, M.G. Vijver, and W. Peijnenburg, Development of a quasi–quantitative structure–activity relationship model for prediction of the immobilization response of Daphnia magna exposed to metal-based nanomaterials, Environ. Toxicol. Chem. 41 (2022), pp. 1439–1450. doi: https://doi.org/10.1002/etc.5322.