References
- N.-E.-H. Hammoudi, W. Sobhi, A. Attoui, T. Lemaoui, A. Erto, and Y. Benguerba, In silico drug discovery of acetylcholinesterase and butyrylcholinesterase enzymes inhibitors based on quantitative structure-activity relationship (QSAR) and drug-likeness evaluation, J. Mol. Struct. 1229 (2021), pp. 129845. doi:https://doi.org/10.1016/j.molstruc.2020.129845.
- J. Qin, W. Lan, Z. Liu, J. Huang, H. Tang, and H. Wang, Synthesis and biological evaluation of 1, 3-dihydroxyxanthone mannich base derivatives as anticholinesterase agents, Chem. Cent. J. 7 (2013), pp. 1–11. doi:https://doi.org/10.1186/1752-153X-7-78.
- M.H. Fatemi and S. Gharaghani, A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine, Bioorg. Med. Chem. 15 (2007), pp. 7746–7754. doi:https://doi.org/10.1016/j.bmc.2007.08.057.
- L.C. Yee and Y.C. Wei, Current modeling methods used in QSAR/QSPR, in Statistical Modeling of Molecular Descriptors in QSAR/QSPR, M. Dehmer, K. Varmuza, and D. Bonchev, eds., Wiley-VCH Verlag GmbH, Weinheim, Germany, 2012, pp. 1–31.
- K. Sadik, S. Byadi, M.E. Hachim, N. El Hamdani, Č. Podlipnik, and A. Aboulmouhajir, Multi-QSAR approaches for investigating the relationship between chemical structure descriptors of Thiadiazole derivatives and their corrosion inhibition performance, J. Mol. Struct. 1240 (2021), pp. 130571. doi:https://doi.org/10.1016/j.molstruc.2021.130571.
- M. Goodarzi, S. Funar-Timofei, and Y. Vander Heyden, Towards better understanding of feature-selection or reduction techniques for quantitative structure–activity relationship models, Trends Analyt. Chem. 42 (2013), pp. 49–63. doi:https://doi.org/10.1016/j.trac.2012.09.008.
- B. Mohseni Bababdani and M. Mousavi, Gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo [4, 5-b]pyridine derivatives, Chemom. Intell. Lab. Syst. 122 (2013), pp. 1–11. doi:https://doi.org/10.1016/j.chemolab.2012.12.002.
- A. Al-Fakih, Z. Algamal, M. Lee, M. Aziz, and H. Ali, QSAR classification model for diverse series of antifungal agents based on improved binary differential search algorithm, SAR QSAR Environ. Res. 30 (2019), pp. 131–143. doi:https://doi.org/10.1080/1062936X.2019.1568298.
- Z.Y. Algamal, M.K. Qasim, M.H. Lee, and H.T.M. Ali, High-dimensional QSAR/QSPR classification modeling based on improving pigeon optimization algorithm, Chemom. Intell. Lab. Syst. 206 (2020), pp. 104170. doi:https://doi.org/10.1016/j.chemolab.2020.104170.
- G. Chandrashekar and F. Sahin, A survey on feature selection methods, Comput. Electr. Eng. 40 (2014), pp. 16–28. doi:https://doi.org/10.1016/j.compeleceng.2013.11.024.
- Y. Wang, J. Wang, H. Liao, and H. Chen, An efficient semi-supervised representatives feature selection algorithm based on information theory, Pattern. Recognit. 61 (2017), pp. 511–523. doi:https://doi.org/10.1016/j.patcog.2016.08.011.
- M. Yuan, Z. Yang, and G. Ji, Partial maximum correlation information: A new feature selection method for microarray data classification, Neurocomputing 323 (2019), pp. 231–243. doi:https://doi.org/10.1016/j.neucom.2018.09.084.
- N.A. Al-Thanoon, O.S. Qasim, and Z.Y. Algamal, A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics, Chemom. Intell. Lab. Syst. 184 (2019), pp. 142–152. doi:https://doi.org/10.1016/j.chemolab.2018.12.003.
- M. Goodarzi, B. Dejaegher, and Y.V. Heyden, Feature selection methods in QSAR studies, J. AOAC. Int. 95 (2012), pp. 636–651. doi:https://doi.org/10.5740/jaoacint.SGE_Goodarzi.
- X.B. Zhou, W.J. Han, J. Chen, and X.Q. Lu, QSAR study on the interactions between antibiotic compounds and DNA by a hybrid genetic-based support vector machine, Monatsh. Chem. 142 (2011), pp. 949–959. doi:https://doi.org/10.1007/s00706-011-0493-7.
- M. Shamsipur, V. Zare-Shahabadi, B. Hemmateenejad, and M. Akhond, An efficient variable selection method based on the use of external memory in ant colony optimization. Application to QSAR/QSPR studies, Anal. Chim. Acta 646 (2009), pp. 39–46. doi:https://doi.org/10.1016/j.aca.2009.05.005.
- X. Zhou, Z. Li, Z. Dai, and X. Zou, QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm, J. Mol. Graph. Model. 29 (2010), pp. 188–196. doi:https://doi.org/10.1016/j.jmgm.2010.06.002.
- Z. Wang, G.L. Durst, R.C. Eberhart, D.B. Boyd, and Z.B. Miled, Particle swarm optimization and neural network application for QSAR, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings, Santa Fe, NM, USA, 2004, pp.194.
- A. Al-Fakih, Z. Algamal, M. Lee, M. Aziz, and H. Ali, A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm, SAR QSAR Environ. Res. 30 (2019), pp. 403–416. doi:https://doi.org/10.1080/1062936X.2019.1607899.
- Y. Gao, S. Zhong, T.L. Torralba-Sanchez, P.G. Tratnyek, E.J. Weber, Y. Chen, and H. Zhang, Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe (II) complex, Water Res. 192 (2021), pp. 116843. doi:https://doi.org/10.1016/j.watres.2021.116843.
- Y. Tian, S. Zhang, H. Yin, and A. Yan, Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods, Chemom. Intell. Lab. Syst. 196 (2020), pp. 103888. doi:https://doi.org/10.1016/j.chemolab.2019.103888.
- A. Saxena and P. Prathipati, Comparison of mlr, pls and ga-mlr in qsar analysis, SAR QSAR Environ. Res. 14 (2003), pp. 433–445. doi:https://doi.org/10.1080/10629360310001624015.
- S. Ajmani, K. Jadhav, and S.A. Kulkarni, Three-dimensional QSAR using the k-nearest neighbor method and its interpretation, J. Chem. Inf. Model. 46 (2006), pp. 24–31. doi:https://doi.org/10.1021/ci0501286.
- U. Norinder, Support vector machine models in drug design: Applications to drug transport processes and QSAR using simplex optimisations and variable selection, Neurocomputing 55 (2003), pp. 337–346. doi:https://doi.org/10.1016/S0925-2312(03)00374-6.
- S. Izrailev and D. Agrafiotis, A novel method for building regression tree models for QSAR based on artificial ant colony systems, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 176–180. doi:https://doi.org/10.1021/ci000336s.
- L.Y. Chuang, S.W. Tsai, and C.H. Yang, Chaotic catfish particle swarm optimization for solving global numerical optimization problems, Appl. Math. Comput. 217 (2011), pp. 6900–6916.
- L.Y. Chuang, S.W. Tsai, and C.H. Yang, Improved binary particle swarm optimization using catfish effect for feature selection, Expert. Syst. Appl. 38 (2011), pp. 12699–12707. doi:https://doi.org/10.1016/j.eswa.2011.04.057.
- K. Héberger, Sum of ranking differences compares methods or models fairly, Trends Analyt. Chem. 29 (2010), pp. 101–109. doi:https://doi.org/10.1016/j.trac.2009.09.009.
- J. Correa-Basurto, C. Flores-Sandoval, J. Marín-Cruz, A. Rojo-Domínguez, L.M. Espinoza-Fonseca, and J.G. Trujillo-Ferrara, Docking and quantum mechanic studies on cholinesterases and their inhibitors, Eur. J. Med. Chem. 42 (2007), pp. 10–19. doi:https://doi.org/10.1016/j.ejmech.2006.08.015.
- M. Frisch, G. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, and G. Petersson, Gaussian 09, revision a. 02, gaussian. Inc, Wallingford, CT, 2009; software available at: http://www.gaussian.com
- P.W. Ayers and W. Yang, Density-functional theory, in Computational Medicinal Chemistry for Drug Discovery, P. Bultinck, H. De Winter, W. Langenaeker, and J.P. Tollenare, eds., CRC Press, New York, 2003, pp. 89–118.
- A.D. Becke, Density‐functional thermochemistry. I. The effect of the exchange‐only gradient correction, J. Chem. Phys. 96 (1992), pp. 2155–2160. doi:https://doi.org/10.1063/1.462066.
- J. Correa-Basurto, M. Bello, M.C. Rosales-Hernandez, M. Hernández-Rodríguez, I. Nicolás-Vázquez, A. Rojo-Domínguez, J.G. Trujillo-Ferrara, R. Miranda, and C. Flores-Sandoval, QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites, Chem. Biol. Interact. 209 (2014), pp. 1–13. doi:https://doi.org/10.1016/j.cbi.2013.12.001.
- R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, DRAGON—Software for the Calculation of Molecular Descriptors, Version 5.5 For Windows, Talete SRL, Milano , Italy, 2007; software available at: http://www.talete.mi.it/index.htm.
- H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, and P.E. Bourne, The protein data bank, Nucleic Acids Res. 28 (2000), pp. 235–242. doi:https://doi.org/10.1093/nar/28.1.235.
- O. Trott and A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem. 31 (2010), pp. 455–461.
- S. Dallakyan and A.J. Olson, Small-molecule library screening by docking with PyRx, Methods. Mol. Biol. 1263 (2015), pp. 243–250.
- Z. Zhou and J.D. Madura, CoMFA 3D-QSAR analysis of HIV-1 RT nonnucleoside inhibitors, TIBO derivatives based on docking conformation and alignment, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 2167–2178. doi:https://doi.org/10.1021/ci049893v.
- B. Kramer, M. Rarey, and T. Lengauer, Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking, Proteins 37 (1999), pp. 228–241. doi:https://doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228::AID-PROT8>3.0.CO;2-8.
- Dassault Systèmes BIOVIA, Discovery Studio Modeling Environment, Dassault Systèmes, San Diego, USA 2016, software available at https://www.3ds.com.
- J.D. Durrant and J.A. McCammon, BINANA: A novel algorithm for ligand-binding characterization, J. Mol. Graph. Model. 29 (2011), pp. 888–893. doi:https://doi.org/10.1016/j.jmgm.2011.01.004.
- R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing/volume II: Appendices, References, Vol. 41, John Wiley & Sons, Weinheim, 2009.
- P.R. Duchowicz, D.O. Bennardi, E.V. Ortiz, and N.C. Comelli, QSAR models for the fumigant activity prediction of essential oils, J. Mol. Graph. Model. 101 (2020), pp. 107751. doi:https://doi.org/10.1016/j.jmgm.2020.107751.
- J. Kennedy and R.C. Eberhart, A discrete binary version of the particle swarm algorithm, 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation, Orlando, FL, USA, 1997, pp. 4104–4108.
- R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39–43.
- Y. Shi and R. Eberhart, A modified particle swarm optimizer, 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), Anchorage, AK, USA, 1998, pp. 69–73.
- Y. Shi and R.C. Eberhart, Parameter selection in particle swarm optimization, International conference on evolutionary programming, Berlin, Heidelberg, 1998, pp. 591–600.
- S. Mirjalili and A. Lewis, S-shaped versus V-shaped transfer functions for binary particle swarm optimization, Swarm. Evol. Comput. 9 (2013), pp. 1–14. doi:https://doi.org/10.1016/j.swevo.2012.09.002.
- Y. Shi and R.C. Eberhart, Empirical study of particle swarm optimization, Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA, 1999, pp. 1945–1950.
- O.S. Qasim, N.A. Al-Thanoon, and Z.Y. Algamal, Feature selection based on chaotic binary black hole algorithm for data classification, Chemom. Intell. Lab. Syst. 204 (2020), pp. 104104. doi:https://doi.org/10.1016/j.chemolab.2020.104104.
- M. Tranmer and M. Elliot, Multiple Linear Regression, Vol. 5, the Cathie Marsh Centre for Census and Survey Research (CCSR), Manchester, UK, 2008.
- O. Kramer, K nearest Neighbors, in Dimensionality Reduction with Unsupervised Nearest Neighbors, J. Kacprzyk and L.C. Jain, eds., in K-nearest Neighbors, Springer Science & Business Media, Berlin, Heidelberg, 2013, pp. 13–23.
- A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276. doi:https://doi.org/10.1016/S1093-3263(01)00123-1.
- H. Drucker, C.J. Burges, L. Kaufman, A.J. Smola, and V. Vapnik, Support vector regression machines, in Advances in Neural Information Processing Systems, M.C. Mozer, M.I. Jordan, and T. Petsche, eds., MIT Press, London, 1997, pp. 155–161.
- V.N. Vapnik, Support vector estimation of functions, in Statistical Learning Theory, S. Haykin, Wiley, New York, 1998, pp. 373–567.
- E. Dehghanian and S. Zare Gheshlaghi, A multiobjective approach in constructing a predictive model for Fischer‐Tropsch synthesis, J. Chemom. 32 (2018), pp. e2969. doi:https://doi.org/10.1002/cem.2969.
- V. Consonni, D. Ballabio, and R. Todeschini, Evaluation of model predictive ability by external validation techniques, J. Chemom. 24 (2010), pp. 194–201. doi:https://doi.org/10.1002/cem.1290.
- P.P. Roy and K. Roy, On some aspects of variable selection for partial least squares regression models, QSAR. Comb. Sci. 27 (2008), pp. 302–313. doi:https://doi.org/10.1002/qsar.200710043.
- P.R. Bevington, Data Reduction and Error Analysis for the Physical Sciences, Vol. 2, McGraw-Hill, New York, 1969.
- K. Kollár-Hunek and K. Héberger, Method and model comparison by sum of ranking differences in cases of repeated observations (ties), Chemom. Intell. Lab. Syst. 127 (2013), pp. 139–146. doi:https://doi.org/10.1016/j.chemolab.2013.06.007.
- K. Héberger and K. Kollár‐Hunek, Sum of ranking differences for method discrimination and its validation: Comparison of ranks with random numbers, J. Chemom. 25 (2011), pp. 151–158. doi:https://doi.org/10.1002/cem.1320.