MIA-QSAR study of the structural merging of (thio)benzamide herbicides with photosynthetic system II inhibitory activities

References

• ACD/ChemSketch. (2018). 2017.2.1 Advanced Chemistry Development, Inc., Toronto.
   Google Scholar
• Barigye, S. J., & Freitas, M. P. (2016). Ten years of the MIA-QSAR strategy: Hystorical development and applications. International Journal of Quantitative Structure-Property Relationships, 1(1), 64–77. https://doi.org/10.4018/IJQSPR.2016010103
   Google Scholar
• Barigye, S. J., Duarte, M. H., Nunes, C. A., & Freitas, M. P. (2016). MIA-plot: A graphical tool for viewing descriptor contributions in MIA-QSAR. RSC Advances, 6(55), 49604–49612. https://doi.org/10.1039/C6RA09593C
   Web of Science ®Google Scholar
• Brookes, G., & Barfoot, P. (2020). GM crop technology use 1996-2018: Farm income and production impacts. GM Crops & Food, 11(4), 242–261. https://doi.org/10.1080/21645698.2020.1779574
   PubMed Web of Science ®Google Scholar
• Chirico, N., & Gramatica, P. (2011). Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. Journal of Chemical Information and Modeling, 51(9), 2320–2335. https://doi.org/10.1021/ci200211n
   PubMed Web of Science ®Google Scholar
• da Cunha, E. F. F., Mancini, D. T., & Ramalho, T. C. (2012). Molecular modeling of the Toxoplasma gondii adenosine kinase inhibitors. Medicinal Chemistry Research, 21(5), 590–600. https://doi.org/10.1007/s00044-011-9554-z
   Web of Science ®Google Scholar
• Daré, J. K., & Freitas, M. P. (2021). Different approaches to encode and model 3D information in a MIA-QSAR perspective. Chemometrics and Intelligent Laboratory Systems, 212, 104286. https://doi.org/10.1016/j.chemolab.2021.104286
   Web of Science ®Google Scholar
• Daré, J. K., Ramalho, T. C., & Freitas, M. P. (2019). 3D perspective into MIA-QSAR: A case for anti-HCV agents. Chemical Biology & Drug Design, 93(6), 1096–1104. https://doi.org/10.1111/cbdd.13440
   PubMed Web of Science ®Google Scholar
• Daré, J. K., Silva, C. F., & Freitas, M. P. (2017). Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data. Ecotoxicology and Environmental Safety, 144, 560–563. https://doi.org/10.1016/j.ecoenv.2017.06.072
   PubMed Web of Science ®Google Scholar
• Daré, J. K., Silva, D. R., Ramalho, T. C., & Freitas, M. P. (2020). Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors. Molecular Simulation, 46(14), 1055–1061. https://doi.org/10.1080/08927022.2020.1800691
   PubMed Web of Science ®Google Scholar
• Dassault Systèmes BIOVIA. (2017). Discovery studio modeling environment, Release 2017. Dassault Systèmes.
   Google Scholar
• Dennington, R. D., Keith, T. A., & Millam, M. J. (2008). GaussView 5.0, Wallingford, CT.
   Google Scholar
• Devine, M., Duke, S. O., & Fedtke, C. (1993). Physiology of herbicide action. Prentice Hall.
   Google Scholar
• Freitas, M. P. (2006). MIA-QSAR modelling of anti-HIV-1 activities of some 2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners . Organic & Biomolecular Chemistry, 4(6), 1154–1159. https://doi.org/10.1039/b516396j
   PubMed Web of Science ®Google Scholar
• Freitas, M. P., Brown, S. D., & Martins, J. A. (2005). MIA-QSAR: a simple 2D image-based approach for quantitative structure–activity relationship analysis. Journal of Molecular Structure, 738(1–3), 149–154. https://doi.org/10.1016/j.molstruc.2004.11.065
   Web of Science ®Google Scholar
• Freitas, M. R., Barigye, S. J., & Freitas, M. P. (2015). Coloured chemical image-based models for the prediction of soil sorption of herbicides. RSC Advances, 5(10), 7547–7553. https://doi.org/10.1039/C4RA12070A
   Web of Science ®Google Scholar
• Fujita, T., & Winkler, D. A. (2016). Understanding the roles of the “two QSARs". Journal of Chemical Information and Modeling, 56(2), 269–274. https://doi.org/10.1021/acs.jcim.5b00229
   PubMed Web of Science ®Google Scholar
• Golbraikh, A., & Tropsha, A. (2002). Beware of q2!. Journal of Molecular Graphics and Modelling, 20(4), 269–276. https://doi.org/10.1016/S1093-3263(01)00123-1
   PubMed Web of Science ®Google Scholar
• Goodarzi, M., & Freitas, M. P. (2008). Predicting boiling points of aliphatic alcohols through multivariate image analysis applied to quantitative structure-property relationships. The Journal of Physical Chemistry. A, 112(44), 11263–11265. https://doi.org/10.1021/jp8059085
   PubMed Web of Science ®Google Scholar
• Honda, I., Yoneyama, K., Iwamura, H., Konnai, M., Takahashi, N., & Yoshida, S. (1990). Structure-activity relationship of 3-nitro-2,4,6-trihydroxybenzamide derivatives in photosynthetic electron transport inhibition. Agricultural and Biological Chemistry, 54(5), 1227–1233. https://doi.org/10.1080/00021369.1990.10870133
   Web of Science ®Google Scholar
• Imramovsky, A., Pesko, M., Ferriz, J. M., Kralova, K., Vinsova, J., & Jampilek, J. (2011). Photosynthesis—Inhibiting efficiency of 4-chloro-2-(chlorophenylcarbamoyl)phenyl alkylcarbamates. Bioorganic & Medicinal Chemistry Letters, 21(15), 4564–4567. https://doi.org/10.1016/j.bmcl.2011.05.118
   PubMed Web of Science ®Google Scholar
• Kennard, R. W., & Stone, L. A. (1969). Computer aided design of experiments. Technometrics, 11(1), 137–148. https://doi.org/10.1080/00401706.1969.10490666
   Web of Science ®Google Scholar
• Kiralj, R., & Ferreira, M. M. C. (2009). Basic validation procedures for regression models in QSAR and QSPR studies: Theory and application. Journal of the Brazilian Chemical Society, 20(4), 770–787. https://doi.org/10.1590/S0103-50532009000400021
   Web of Science ®Google Scholar
• Mitra, I., Saha, A., & Roy, K. (2010). Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Molecular Simulation, 36(13), 1067–1079. https://doi.org/10.1080/08927022.2010.503326
   Web of Science ®Google Scholar
• Nunes, C. A., & Freitas, M. P. (2013). Introducing new dimensions in MIA-QSAR: A case for chemokine receptor inhibitors. European Journal of Medicinal Chemistry, 62, 297–300. https://doi.org/10.1016/j.ejmech.2013.01.005
   PubMed Web of Science ®Google Scholar
• Nunes, C. A., Freitas, M. P., Pinheiro, A. C. M., & Bastos, S. C. (2012). Chemoface: A novel free user-friendly interface for chemometrics. Journal of the Brazilian Chemical Society, 23(11), 2003–2010. https://doi.org/10.1590/S0103-50532012005000073
   Web of Science ®Google Scholar
• Oettmeier, W. (2003). Herbicides, inhibitors of photosynthesis at photosystem II. In J. R. Plimmer, N. R. Ragsdale, & D. W. Gammon (Eds.), Encyclopedia of agrochemicals. John Wiley and Sons.
   Google Scholar
• Pereira, I. V., & Freitas, M. P. (2021). Double focus in the modelling of anti-influenza properties of 2-iminobenzimidazolines: pharmacology and toxicology. SAR and QSAR in Environmental Research, 32(8), 643–654. https://doi.org/10.1080/1062936X.2021.1950832
   PubMed Web of Science ®Google Scholar
• Ragno, R. (2019). www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices-the Py-CoMFA web application as tool to build models from pre-aligned datasets. Journal of Computer-Aided Molecular Design, 33(9), 855–864. https://doi.org/10.1007/s10822-019-00231-x
   PubMed Web of Science ®Google Scholar
• Ramalho, T. C., França, T. C. C., Rennó, M. N., Guimarães, A. P., da Cunha, E. F. F., & Kuca, K. (2010). Development of new acetylcholinesterase reactivators: Molecular modeling versus in vitro data. Chemico-Biological Interactions, 185(1), 73–77. https://dx.doi.org/10.1016/j.cbi.2010.05.016
   PubMed Web of Science ®Google Scholar
• Roy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N. (2013). Some case studies on application of "r(m)2" metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. Journal of Computational Chemistry, 34(12), 1071–1082. https://doi.org/10.1002/jcc.23231
   PubMed Web of Science ®Google Scholar
• Roy, K., Das, R. N., Ambure, P., & Aher, R. B. (2016). Be aware of error measures. Further studies on validation and predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, 152, 18–33. https://doi.org/10.1016/j.chemolab.2016.01.008
   Web of Science ®Google Scholar
• Roy, K., Kar, S., & Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 145, 22–29. https://doi.org/10.1016/j.chemolab.2015.04.013
   Web of Science ®Google Scholar
• Sharma, M. C. (2016). Identification of 3-nitro-2,4,6-trihydroxybenzamide derivatives as photosynthetic electron transport inhibitors by QSAR and pharmacophore studies. Interdisciplinary Sciences, Computational Life Sciences, 8(2), 109–121. https://doi.org/10.1007/s12539-015-0019-9
   PubMed Web of Science ®Google Scholar
• Storkey, J., Bruce, T. J. A., McMillan, V. E., & Neve, P. (2019). The future of sustainable crop protection relies on increased diversity of cropping systems and landscapes. In G. Lemaire, S. Kronberg, P. C. F. Carvalho, & S. Recous (Eds.), Agroecosystem diversity (pp. 199–209). Elsevier B.V.
   Google Scholar
• Thomsen, R., & Christensen, M. H. (2006). MolDock: A new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry, 49(11), 3315–3321. https://doi.org/10.1021/jm051197e
   PubMed Web of Science ®Google Scholar
• Todeschini, R., Consonni, V., Ballabio, D., & Grisoni, F. (2020). Chemometrics for QSAR modeling. In S. D. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive chemometrics (pp. 599–634). Elsevier.
   Google Scholar
• Wang, Y.-L., Wang, F., Shi, X.-X., Jia, C.-Y., Wu, F.-X., Hao, G.-F., & Yang, G.-F. (2021). Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery. Brief. Bioinf, 22, bbaa276. https://doi.org/10.1093/bib/bbaa276
   PubMed Web of Science ®Google Scholar
• Wei, X., Su, X., Cao, P., Liu, X., Chang, W., Li, M., Zhang, X., & Liu, Z. (2016). Structure of spinach photosystem II-LHCII supercomplex at 3.2 Å resolution . Nature, 534(7605), 69–74. https://doi.org/10.1038/nature18020
   PubMed Web of Science ®Google Scholar