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Towards the Revival of Interpretable QSAR Models

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Advances in QSAR Modeling

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 24))

Abstract

Quantitative structure-activity relationship (QSAR) has been instrumental in aiding medicinal chemists and physical scientists in understanding how modification of substituents at different positions on a molecular structure exert its influence on the observed biological activity and physicochemical property, respectively. QSAR has received great attention owing to its predictive capability and as such efforts had been directed toward obtaining models with high prediction performance. However, to be useful QSAR models need to be informative and interpretable in which the underlying molecular features that contribute to the increase or decrease of the biological activity are revealed by the model. Thus, the aim of this chapter is to briefly review the general concepts of QSAR modeling, its development and discussions on key issues influencing and contributing to the interpretability of QSAR models.

W. Shoombuatong and P. Prathipati

These authors contributed equally to this work.

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References

  • Alcal-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., Garca, S., Snchez, L., et al. (2011). Journal of Multiple-Valued Logic and Soft Computing, 17, 255.

    Google Scholar 

  • Andrews, D. F. (1974). Technometrics, 16(4), 523.

    Article  Google Scholar 

  • Andrews, R., Diederich, J., & Tickle, A. B. (1995). Knowledge-Based Systems, 8(6), 373.

    Article  Google Scholar 

  • Augasta, M. G., & Kathirvalavakumar, T. (2012). Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, Salem, Tamilnadu (pp. 21–23).

    Google Scholar 

  • Backman, T. W., Cao, Y., & Girke, T. (2011). Nucleic Acids Research, 39, W486.

    Article  CAS  Google Scholar 

  • Baell, J. B., & Holloway, G. A. (2010). Journal of Medicinal Chemistry, 53(7), 2719.

    Article  CAS  Google Scholar 

  • Bajorath, J. (2014). Molecular Informatics, 33(6–7), 438.

    Article  CAS  Google Scholar 

  • Balaban, A. T. (1982). Chemical Physics Letters, 89(5), 399.

    Article  CAS  Google Scholar 

  • Barakat, N. H., & Bradley, A. P. (2007). IEEE Transactions on Knowledge and Data Engineering, 19(6), 729.

    Article  Google Scholar 

  • Barakat, N., & Bradley, A. P. (2010). Neurocomputing, 74(1), 178.

    Article  Google Scholar 

  • Barakat, N., & Diederich, J. (2004). 14th International Conference on Computer Theory and Applications (ICCTA’2004). Alexandria, Egypt.

    Google Scholar 

  • Barakat, N., & Diederich, J. (2005). International Journal of Computational Intelligence, 2(1), 59.

    Google Scholar 

  • Benfenati, E., Toropov, A. A., Toropova, A. P., Manganaro, A., & Gonella, D. R. (2011). Chemical Biology and Drug Design, 77(6), 471.

    Article  CAS  Google Scholar 

  • Bengio, Y. (2009). Foundations and Trends in Machine Learning, 2(1), 1.

    Article  Google Scholar 

  • Borman, S. (1990). Chemical and Engineering News, 68(8), 20.

    Article  Google Scholar 

  • Bordes, A., Chopra, S., & Weston, J. (2014). ar**v preprint: ar**v:1406.3676.

  • Bravi, G., Gancia, E., Mascagni, P., Pegna, M., Todeschini, R., & Zaliani, A. (1997). Journal of Computer-Aided Molecular Design, 11(1), 79.

    Article  CAS  Google Scholar 

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. New York, USA: CRC Press.

    Google Scholar 

  • Breiman, L. (2001). Machine Learning, 45(1), 5.

    Article  Google Scholar 

  • Broto, P., Moreau, G., & Vandycke, C. (1984). European Journal of Medicinal Chemistry, 19(1), 66.

    CAS  Google Scholar 

  • Brown, N., McKay, B., & Gasteiger, J. (2006). Journal of Computer-Aided Molecular Design, 20(5), 333.

    Article  CAS  Google Scholar 

  • Brustle, M., Beck, B., Schindler, T., King, W., Mitchell, T., & Clark, T. (2002). Journal of Medicinal Chemistry, 45(16), 3345.

    Article  CAS  Google Scholar 

  • Burges, C. J. (1998). Data Mining and Knowledge Discovery, 2(2), 121.

    Article  Google Scholar 

  • Cao, D. S. (2017a). ChemDes: An integrated web-based platform for molecular descriptor and fingerprint computation. http://www.scbdd.com/chemdes.

  • Cao, D. S. (2017b). ChemoPy Descriptor Calculator. http://www.scbdd.com/chemopy_desc/index/.

  • Cao, Y., Charisi, A., Cheng, L. C., Jiang, T., & Girke, T. (2008). Bioinformatics, 24(15), 1733.

    Article  CAS  Google Scholar 

  • Cao, D., Liang, Y., Xu, Q., Yun, Y., & Li, H. (2011). Journal of Computer-Aided Molecular Design, 25(1), 67.

    Article  CAS  Google Scholar 

  • Cao, D. S., Xu, Q. S., Hu, Q. N., & Liang, Y. Z. (2013). Bioinformatics, 29(8), 1092.

    Article  CAS  Google Scholar 

  • CAMO Software AS. (2017). The Unscrambler. http://www.camo.com/rt/Products/Unscrambler/unscrambler.html.

  • Capuzzi, S. J., Politi, R., Isayev, O., Farag, S., & Tropsha, A. (2016). Frontiers of Environmental Science, 4, 3.

    Article  Google Scholar 

  • Certara. (2017). Benchware 3D Explorer. https://www.certara.com/software/molecular-modeling-and-simulation/benchware-3d-explorer/.

  • Chang, C. C., & Lin, C. J. (2011). ACM Transactions on Intelligent Systems and Technology, 2(27), 1.

    Google Scholar 

  • Charoenkwan, P., Shoombuatong, W., Lee, H. C., Chaijaruwanich, J., Huang, H. L., & Ho, S. Y. (2013). PLoS One, 8(9), e72368.

    Article  CAS  Google Scholar 

  • Chemical Computing Group Inc. (2017). Molecular Operating Environment (MOE). https://www.chemcomp.com/MOE-Cheminformatics_and_QSAR.htm.

  • Chen, H., Carlsson, L., Eriksson, M., Varkonyi, P., Norinder, U., & Nilsson, I. (2013). Journal of Chemical Information and Modeling, 53(6), 1324.

    Article  CAS  Google Scholar 

  • Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., et al. (2014). Journal of Medicinal Chemistry, 57(12), 4977.

    Article  CAS  Google Scholar 

  • Chiu, Y. Y., Lin, C. T., Huang, J. W., Hsu, K. C., Tseng, J. H., You, S. R., et al. (2013). Nucleic Acids Research, 41(Database issue), D430.

    Google Scholar 

  • Churchwell, C. J., Rintoul, M. D., Martin, S., Visco, D. P., Kotu, A., Larson, R. S., et al. (2004). Journal of Molecular Graphics and Modelling, 22(4), 263.

    Article  CAS  Google Scholar 

  • Ciodaro, T., Deva, D., De Seixas, J., & Damazio, D. (2012). Journal of Physics: Conference Series, 368, 012030. IOP Publishing.

    Google Scholar 

  • Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Journal of Machine Learning Research, 12, 2493.

    Google Scholar 

  • Consonni, V., Todeschini, R., & Pavan, M. (2002). Journal of Chemical Information and Computer Sciences, 42(3), 682.

    Article  CAS  Google Scholar 

  • Cortes-Ciriano, I., Ain, Q. U., Subramanian, V., Lenselink, E. B., Mendez-Lucio, O., IJzerman, A. P., et al. (2015). Medicinal Chemical Communications, 6, 24.

    Article  CAS  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Machine Learning, 20(3), 273.

    Google Scholar 

  • Costello, J. C., Heiser, L. M., Georgii, E., Gonen, M., Menden, M. P., Wang, N. J., et al. (2014). Nature Biotechnology, 32(12), 1202.

    Article  CAS  Google Scholar 

  • Cox, R., Green, D. V., Luscombe, C. N., Malcolm, N., & Pickett, S. D. (2013). Journal of Computer-Aided Molecular Design, 27(4), 321.

    Article  CAS  Google Scholar 

  • Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988). Journal of the American Chemical Society, 110(18), 5959.

    Article  CAS  Google Scholar 

  • Craven, M. W., & Shavlik, J. W. (1996). Advances in neural information processing systems (pp. 24–30). Cambridge, USA: MIT Press.

    Google Scholar 

  • Cros, A. F. A. (1863). Action de lalcohol amylique sur lorganisme. Ph.D. thesis, University of Strasbourg.

    Google Scholar 

  • Crum-Brown, A., & Fraser, T. (1868). Transactions of the Royal Society of Edinburgh, 25, 151.

    Article  Google Scholar 

  • Danishuddin, A. U. K. (2016). Drug Discovery Today, 21(8), 1291.

    Article  CAS  Google Scholar 

  • Dearden, J., Cronin, M., & Kaiser, K. (2009). SAR and QSAR in Environmental Research, 20(3–4), 241.

    Article  CAS  Google Scholar 

  • de Vries, S. J., van Dijk, M., & Bonvin, A. M. (2010). Nature Protocols, 5(5), 883.

    Article  CAS  Google Scholar 

  • Destrero, A., Mosci, S., De Mol, C., Verri, A., & Odone, F. (2009). Computational Management Science, 6(1), 25.

    Article  Google Scholar 

  • Devinyak, O., Havrylyuk, D., & Lesyk, R. (2014). Journal of Computer-Aided Molecular Design, 54, 194.

    CAS  Google Scholar 

  • Dimova, D., & Bajorath, J. (2016). Molecular Informatics, 35(5), 181.

    Article  CAS  Google Scholar 

  • Dimitrov, S. D., Didericj, R., Sobanski, T., Pavlov, T. S., Chapkov, G. V., Chapkonov, A. S., et al. (2016). SAR and QSAR in Environmental Research, 1–17.

    Google Scholar 

  • Dong, J., Cao, D. S., Miao, H. Y., Liu, S., Deng, B. C., Yun, Y. H., et al. (2015). Journal of Cheminformatics, 7, 60.

    Article  CAS  Google Scholar 

  • Doweyko, A. M. (2004). Journal of Computer-Aided Molecular Design, 18(7), 587.

    Article  CAS  Google Scholar 

  • Doweyko, A. M. (2008). Journal of Computer-Aided Molecular Design, 22(2), 81.

    Article  CAS  Google Scholar 

  • Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., Vapnik, V. (1996). Proceedings of the 9th International Conference on Neural Information Processing Systems, NIPS’96 (pp. 155–161). Cambridge, MA, USA: MIT Press.

    Google Scholar 

  • DTAI Research Group (2017). DMax Chemistry Assistant. https://dtai.cs.kuleuven.be/software/dmax/.

  • Durant, J. L., Leland, B. A., Henry, D. R., & Nourse, J. G. (2002). Journal of Chemical Information and Computer Sciences, 42(6), 1273.

    Article  CAS  Google Scholar 

  • Ebrahimi, E., Monjezi, M., Khalesi, M. R., & Armaghani, D. J. (2016). Bulletin of Engineering Geology and the Environment, 75(1), 27.

    Article  Google Scholar 

  • Eklund, M., Norinder, U., Boyer, S., & Carlsson, L. (2012). In L. Iliadis, I. Maglogiannis, H. Papadopoulos, K. Karatzas, & S. Sioutas (Eds.), Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27–30, 2012, Proceedings, Part II (pp. 166–175). Berlin, Germany: Springer.

    Google Scholar 

  • Eklund, M., Norinder, U., Boyer, S., & Carlsson, L. (2014). Journal of Chemical Information and Modeling, 54(3), 837.

    Article  CAS  Google Scholar 

  • Eriksson, M., Chen, H., Carlsson, L., Nissink, J. W., Cumming, J. G., & Nilsson, I. (2014). Journal of Chemical Information and Modeling, 54(4), 1117.

    Article  CAS  Google Scholar 

  • Esbensen, K. H., & Geladi, P. (2010). Journal of Chemometrics, 24(3–4), 168.

    Article  CAS  Google Scholar 

  • Faulon, J. L. (1994). Journal of Chemical Information and Computer Sciences, 34(5), 1204.

    CAS  Google Scholar 

  • Faulon, J. L. (1996). Journal of Chemical Information and Computer Sciences, 36(4), 731.

    Article  CAS  Google Scholar 

  • Faulon, J. L., Churchwell, C. J., & Visco, D. P. (2003). Journal of Chemical Information and Computer Sciences, 43(3), 721.

    Article  CAS  Google Scholar 

  • Faulon, J. L., Collins, M. J., & Carr, R. D. (2004). Journal of Chemical Information and Computer Sciences, 44(2), 427.

    Article  CAS  Google Scholar 

  • Faulon, J. L., Brown, W. M., & Martin, S. (2005). Journal of Computer-Aided Molecular Design, 19(9–10), 637.

    Article  CAS  Google Scholar 

  • Feng, B. Y., Shelat, A., Doman, T. N., Guy, R. K., & Shoichet, B. K. (2005). Nature Chemical Biology, 1(3), 146.

    Article  CAS  Google Scholar 

  • Feng, B. Y., Simeonov, A., Jadhav, A., Babaoglu, K., Inglese, J., Shoichet, B. K., et al. (2007). Journal of Medicinal Chemistry, 50(10), 2385.

    Article  CAS  Google Scholar 

  • Feng, B. Y., & Shoichet, B. K. (2006). Nature Protocols, 1(2), 550.

    Article  CAS  Google Scholar 

  • Filimonov, D. A., Zakharov, A. V., Lagunin, A. A., & Poroikov, V. V. (2009). SAR and QSAR in Environmental Research, 20(7), 679.

    Article  CAS  Google Scholar 

  • Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., et al. (2014). Chemistry of Heterocyclic Compounds, 50(3), 444.

    Article  CAS  Google Scholar 

  • Frank, E., Hall, M. & Trigg, L. Weka. http://www.cs.waikato.ac.nz/ml/weka/.

  • Free, S. M., & Wilson, J. W. (1964). Journal of Medicinal Chemistry, 7(4), 395.

    Article  CAS  Google Scholar 

  • Fu, X., Ong, C., Keerthi, S., Hung, G. G., & Goh, L. (2004). In Proceedings of IEEE International Joint Conference on Neural Networks (pp. 291–296). Budapest, Hungary: IEEE.

    Google Scholar 

  • Fujita, T., & Winkler, D. A. (2016). Journal of Chemical Information and Modeling, 56(2), 269.

    Article  CAS  Google Scholar 

  • Fung, G., Sandilya, S., & Rao, R. B. (2005). Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (pp. 32–40). New York, USA: ACM.

    Google Scholar 

  • Gallup, G. A., Gilkerson, W., & Jones, M. (1952). Transactions of the Kansas Academy of Science, 55(2), 232.

    Article  CAS  Google Scholar 

  • Gao, H., Katzenellenbogen, J. A., Garg, R., & Hansch, C. (1999). Chemical Reviews, 99(3), 723.

    Article  CAS  Google Scholar 

  • Garcia-Jacas, C. R., Marrero-Ponce, Y., Acevedo-Martinez, L., Barigye, S. J., Valdes-Martini, J. R., & Contreras-Torres, E. (2014). Journal of Computational Chemistry, 35(18), 1395.

    Article  CAS  Google Scholar 

  • Garg, R., Gupta, S. P., Gao, H., Babu, M. S., Debnath, A. K., & Hansch, C. (1999). Chemical Reviews, 99(12), 3525.

    Article  CAS  Google Scholar 

  • Garg, R., Kurup, A., Mekapati, S. B., & Hansch, C. (2003). Chemical Reviews, 103(3), 703.

    Article  CAS  Google Scholar 

  • Geronikaki, A. A., Lagunin, A. A., Hadjipavlou-Litina, D. I., Eleftheriou, P. T., Filimonov, D. A., Poroikov, V. V., et al. (2008). Journal of Medicinal Chemistry, 51(6), 1601.

    Article  CAS  Google Scholar 

  • Girke, T. (2017). ChemmineR: Cheminformatics toolkit for R. https://www.bioconductor.org/packages/release/bioc/html/ChemmineR.html.

  • Gleeson, M. P. (2008). Journal of Medicinal Chemistry, 51(4), 817.

    Article  CAS  Google Scholar 

  • Gobbi, M., Beeg, M., Toropova, M. A., Toropov, A. A., & Salmona, M. (2016). Toxicology Letters, 250, 42.

    Article  CAS  Google Scholar 

  • Golbraikh, A., Fourches, D., Sedykh, A., Muratov, E., Liepina, I., & Tropsha, A. (2014). Practical aspects of computational chemistry III (pp. 187–230). Boston, USA: Springer.

    Google Scholar 

  • Gong, R., Huang, S. H., & Chen, T. (2008). IEEE Transactions on Industrial Informatics, 4(3), 198.

    Article  Google Scholar 

  • Gonzlez, M. P., Tern, C., Fall, Y., Teijeira, M., & Besada, P. (2005). Bioorganic and Medicinal Chemistry, 13(3), 601.

    Article  CAS  Google Scholar 

  • Goodarzi, M., Heyden, Y. V., & Funar-Timofei, S. (2013). Trends in Analytical Chemistry, 42, 49.

    Article  CAS  Google Scholar 

  • Gozalbes, R., Doucet, J. P., & Derouin, F. (2002). Current Drug Targets Infectious Disorders, 2(1), 93.

    Article  CAS  Google Scholar 

  • Gramatica, P., Chirico, N., Papa, E., Cassani, S., & Kovarich, S. (2013). Journal of Computational Chemistry, 34(24), 2121.

    Article  CAS  Google Scholar 

  • Guha, R. (2017). CDK Descriptor Calculator GUI (version 1.4. 6). http://www.rguha.net/code/java/cdkdesc.html.

  • Guha, R., & Van Drie, J. H. (2008). Journal of Chemical Information and Modeling, 48(8), 1716.

    Article  CAS  Google Scholar 

  • Gupta, A., Park, S., & Lam, S. M. (1999). IEEE Transactions on Knowledge and Data Engineering, 11(6), 985.

    Article  Google Scholar 

  • Gütlein, M., Helma, C., Karwath, A., & Kramer, S. (2013). Molecular Informatics, 32(5–6), 516.

    Article  CAS  Google Scholar 

  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Machine Learning, 46(1–3), 389.

    Article  Google Scholar 

  • Guyon, I. (2003). Journal of Machine Learning Research, 3, 1157.

    Google Scholar 

  • Hadjipavlou-Litina, D., Garg, R., & Hansch, C. (2004). Chemical Reviews, 104(9), 3751.

    Article  CAS  Google Scholar 

  • Hall, M. A. (1999). Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato.

    Google Scholar 

  • Hammett, L. P. (1937). Journal of the American Chemical Society, 59(1), 96.

    Article  CAS  Google Scholar 

  • Hansch, C., Maloney, P. P., Fujita, T., & Muir, R. M. (1962). Nature, 194, 178.

    Article  CAS  Google Scholar 

  • Hansch, C., Leo, A., & Taft, R. (1991). Chemical Reviews, 91(2), 165.

    Article  CAS  Google Scholar 

  • Hansch, C., Hoekman, D., & Gao, H. (1996). Chemical Reviews, 96(3), 1045.

    Article  CAS  Google Scholar 

  • Hansch, C., Hoekman, D., Leo, A., Weininger, D., & Selassie, C. D. (2002). Chemical Reviews, 102(3), 783.

    Article  CAS  Google Scholar 

  • Hansch, C. (2011). Journal of Computer-Aided Molecular Design, 25(6), 495.

    Article  CAS  Google Scholar 

  • Hansch, C., & Gao, H. (1997). Chemical Reviews, 97(8), 2995.

    Article  CAS  Google Scholar 

  • Harding, A. P., Wedge, D. C., & Popelier, P. L. (2009). Journal of Chemical Information and Modeling, 49(8), 1914.

    Article  CAS  Google Scholar 

  • Hawkins, D. M., Basak, S. C., & Mills, D. (2003). Journal of Chemical Information and Computer Sciences, 43(2), 579.

    Article  CAS  Google Scholar 

  • Héberger, K., & Rajkó, R. (2002). Journal of Chemometrics, 16(8), 436.

    Article  CAS  Google Scholar 

  • Heberger, K., & Skrbic, B. (2012). Analytica Chimica Acta, 716, 92.

    Article  CAS  Google Scholar 

  • Helland, I. S. (1988). Communication in Statistics: Simulation and Computation, 17(2), 581.

    Article  Google Scholar 

  • Helland, I. S. (2001). Chemometrics and Intelligent Laboratory, 58(2), 97.

    Article  CAS  Google Scholar 

  • Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013). Nature, 500(7461), 168.

    Article  CAS  Google Scholar 

  • Hinselmann, G., Rosenbaum, L., Jahn, A., Fechner, N., & Zell, A. (2011). Journal of Cheminformatics, 3(1), 3.

    Article  CAS  Google Scholar 

  • Hinselmann, G., Rosenbaum, L., Jahn, A., Fechner, N., & Zell, A. (2017). jCompoundMapper: An open source java library and command-line tool for chemical fingerprints. http://jcompoundmapper.sourceforge.net/.

  • Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). Neural Computing, 18(7), 1527.

    Article  Google Scholar 

  • Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. (2012). ar**v preprint ar**v:1207.0580.

  • Hosmer, D. W, Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (pp. 1–33). New Jersey, USA: Wiley.

    Google Scholar 

  • Hu, X., Hu, Y., Vogt, M., Stumpfe, D., & Bajorath, J. (2012). Journal of Chemical Information and Modeling, 52(5), 1138.

    Article  CAS  Google Scholar 

  • IBM. (2017). IBM SPSS Software. http://www.ibm.com/analytics/us/en/technology/spss/.

  • Jarvis, R. M., Broadhurst, D., Johnson, H., O’Boyle, N. M., & Goodacre, R. (2006). Bioinformatics, 22(20), 2565.

    Article  CAS  Google Scholar 

  • Jelfs, S., Ertl, P., & Selzer, P. (2007). Journal of Chemical Information and Modeling, 47(2), 450.

    Article  CAS  Google Scholar 

  • Johnson, S. R. (2008). Journal of Chemical Information and Modeling, 48(1), 25.

    Article  CAS  Google Scholar 

  • Jolliffe, I. (2002). Principal component analysis. New York, USA: Springer.

    Google Scholar 

  • Katritzky, A. R., Kuanar, M., Slavov, S., Hall, C. D., Karelson, M., Kahn, I., et al. (2010). Chemical Reviews, 110(10), 5714.

    Article  CAS  Google Scholar 

  • Khan, M. T., & Sylte, I. (2007). Current Drug Discovery Technologies, 4(3), 141.

    Article  CAS  Google Scholar 

  • Kier, L. B., & Hall, L. H. (1976). Molecular connectivity in chemistry and drug research. New York, USA: Academic Press.

    Google Scholar 

  • Kim, K. H. (2007a). Journal of Computer-Aided Molecular Design, 21(8), 421.

    Google Scholar 

  • Kim, K. H. (2007b). Journal of Computer-Aided Molecular Design, 21(1–3), 63.

    Google Scholar 

  • Kim, D., & Lee, J. (2000). In López de Mántaras and Plaza (Eds.), Proceedings of the 11th European conference on machine learning (pp. 211–219). London, UK: Springer.

    Google Scholar 

  • Kohonen, T. (2017). SOM: Self-Organization Map. http://www.cis.hut.fi/somtoolbox/.

  • Krasavin, M. (2015). European Journal of Medicinal Chemistry, 97, 525.

    Article  CAS  Google Scholar 

  • Kubinyi, H. (1988). Quantitative Structure-Activity Relationship, 7(3), 121.

    Article  CAS  Google Scholar 

  • Kubinyi, H. (1993). 3D QSAR in drug design: Volume 1: Theory methods and applications (Vol. 1). Dordrecht, Netherlands: Springer Science & Business Media.

    Google Scholar 

  • Kubinyi, H. (2006). In S. Ekins (Ed.) Computer applications in pharmaceutical research and development (pp. 377–424). New Jersey, USA: Wiley.

    Google Scholar 

  • Kufareva, I., & Abagyan, R. (2008). Journal of Medicinal Chemistry, 51(24), 7921.

    Article  CAS  Google Scholar 

  • Kuhn, T., Willighagen, E. L., Zielesny, A., & Steinbeck, C. (2010). BMC Bioinformatics, 11, 159.

    Article  CAS  Google Scholar 

  • Kurgan, L., Razib, A. A., Aghakhani, S., Dick, S., Mizianty, M., & Jahandideh, S. (2009). BMC Structural Biology, 9, 50.

    Article  CAS  Google Scholar 

  • Kurup, A., Garg, R., & Hansch, C. (2000). Chemical Reviews, 100(3), 909.

    Article  CAS  Google Scholar 

  • Kurup, A., Garg, R., Carini, D. J., & Hansch, C. (2001). Chemical Reviews, 101(9), 2727.

    Article  CAS  Google Scholar 

  • Kurup, A., Garg, R., & Hansch, C. (2001). Chemical Reviews, 101(8), 2573.

    Article  CAS  Google Scholar 

  • Kvasnicka, V., & Pospichal, J. (1996). Journal of Chemical Information and Computer Sciences, 36(3), 516.

    Article  CAS  Google Scholar 

  • Lawrence, D., et al. (1991). Handbook of genetic algorithms. New York, USA: Van No Strand Reinhold.

    Google Scholar 

  • Leung, M. K., **ong, H. Y., Lee, L. J., & Frey, B. J. (2014). Bioinformatics, 30(12), i121.

    Article  CAS  Google Scholar 

  • Li, Q., Wang, Y., & Bryant, S. H. (2009). Bioinformatics, 25(24), 3310.

    Article  CAS  Google Scholar 

  • Lipnick, R. L. (1991). Studies of narcosis. Dordrecht, Netherlands: Springer.

    Google Scholar 

  • Liu, S. S., Yin, C. S., Li, Z. L., & Cai, S. X. (2001). Journal of Chemical Information and Computer Sciences, 41(2), 321.

    Article  CAS  Google Scholar 

  • Liu, H., & Motoda, H. (2007). Computational methods of feature selection. Boca Raton, Florida: CRC Press.

    Google Scholar 

  • Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015). Journal of Chemical Information and Modeling, 55(2), 263.

    Article  CAS  Google Scholar 

  • Manallack, D. T. (2008). Perspectives in Medicinal Chemistry, 1, 25.

    Google Scholar 

  • Maplesoft. (2017). Maple. https://www.maplesoft.com/products/Maple/.

  • Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). European Journal of Operational Research, 183(3), 1466.

    Article  Google Scholar 

  • Masand, V. H., Toropov, A. A., Toropova, A. P., & Mahajan, D. T. (2014). Current Computer-Aided Drug Design, 10, 75.

    Article  CAS  Google Scholar 

  • Mazanetz, M. P., Marmon, R. J., Reisser, C. B., & Morao, I. (2012). Current Topics in Medicinal Chemistry, 12(8), 1965.

    Article  CAS  Google Scholar 

  • McGovern, S. L., Caselli, E., Grigorieff, N., & Shoichet, B. K. (2002). Journal of Medicinal Chemistry, 45(8), 1712.

    Article  CAS  Google Scholar 

  • Medina Marrero, R., Marrero-Ponce, Y., Barigye, S. J., Echeverria Diaz, Y., Acevedo-Barrios, R., Casanola-Martin, G. M., et al. (2015). SAR and QSAR in Environmental Research, 26(11), 943.

    Article  CAS  Google Scholar 

  • Miller, B. T., Singh, R. P., Klauda, J. B., Hodoscek, M., Brooks, B. R., & Woodcock, H. L. (2008). Journal of Chemical Information and Modeling, 48(9), 1920.

    Article  CAS  Google Scholar 

  • Molplex Ltd., & Sykora, V. (2017). Chemical Descriptors Library (CDL). https://sourceforge.net/projects/cdelib/.

  • Morgenthaler, M., Schweizer, E., Hoffmann-Roder, A., Benini, F., Martin, R. E., Jaeschke, G., et al. (2007). ChemMedChem, 2(8), 1100.

    Article  CAS  Google Scholar 

  • Nantasenamat, C., Naenna, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2005). Journal of Computer-Aided Molecular Design, 19(7), 509.

    Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2007a). Biosensors and Bioelectronics, 22(12), 3309.

    Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., Tansila, N., Naenna, T., & Prachayasittikul, V. (2007b). Journal of Computational Chemistry, 28(7), 1275.

    Google Scholar 

  • Nantasenamat, C., Piacham, T., Tantimongcolwat, T., Naenna, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2008). Journal of Biological Systems, 16(02), 279.

    Article  CAS  Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2009). EXCLI Journal, 8(7), 74.

    Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2010). Expert Opinion on Drug Discovery, 5(7), 633.

    Article  CAS  Google Scholar 

  • Nantasenamat, C., Worachartcheewan, A., Jamsak, S., Preeyanon, L., Shoombuatong, W., Simeon, S., et al. (2015). In H. Cartwright (Ed.), Artificial neural networks (pp. 119–147). New York, NY, USA: Springer.

    Google Scholar 

  • Nantasenamat, C., & Prachayasittikul, V. (2015). Expert Opinion on Drug Discovery, 10(4), 321.

    Article  CAS  Google Scholar 

  • NeuralWare. (2017). NeuralWare. http://www.neuralware.com/.

  • Núñez, H., Angulo, C., & Català, A. (2002). 10th European Symposium on Artificial Neural Networks (ESANN), pp. 107–112.

    Google Scholar 

  • O’Boyle, N. M., & Hutchison, G. R. (2008). Chemistry Central Journal, 2, 24.

    Google Scholar 

  • O’Boyle, N. M., Morley, C., & Hutchison, G. R. (2008). Chemistry Central Journal, 2, 5.

    Google Scholar 

  • O’Boyle, N. M., Morley, C. & Hutchison, G. R. (2017a). Pybel. https://openbabel.org/docs/dev/UseTheLibrary/Python_Pybel.html.

  • O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Journal of Cheminformatics, 3, 33.

    Google Scholar 

  • O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2017b). Open Babel: The open source chemistry toolbox. http://openbabel.org/.

  • Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., et al. (2008). Nature Neuroscience, 11(11), 1271.

    Google Scholar 

  • Patani, G. A., & LaVoie, E. J. (1996). Chemical Reviews, 96(8), 3147.

    Article  CAS  Google Scholar 

  • Patlewicz, G., Jeliazkova, N., Safford, R. J., Worth, A. P., & Aleksiev, B. (2008). SAR and QSAR in Environmental Research, 19(5–6), 495.

    Article  CAS  Google Scholar 

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M. et al. (2017). Scikit-learn. http://scikit-learn.org/.

  • Peng, H., Long, F., & Ding, C. (2005). IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226.

    Article  Google Scholar 

  • Poroikov, V. V., Filimonov, D. A., Ihlenfeldt, W. D., Gloriozova, T. A., Lagunin, A. A., Borodina, Y. V., et al. (2003). Journal of Chemical Information and Computer Sciences, 43(1), 228.

    Article  CAS  Google Scholar 

  • Prachayasittikul, V., Worachartcheewan, A., Shoombuatong, W., Songtawee, N., Simeon, S., Prachayasittikul, V., et al. (2015). Current Topics in Medicinal Chemistry, 15(18), 1780.

    Article  CAS  Google Scholar 

  • Prathipati, P., Pandey, G., & Saxena, A. K. (2005). Journal of Chemical Information and Modeling, 45(1), 136.

    Article  CAS  Google Scholar 

  • Prathipati, P., Dixit, A., & Saxena, A. K. (2007). Journal of Computer-Aided Molecular Design, 92, 29.

    Google Scholar 

  • Prathipati, P., Ma, N. L., & Keller, T. H. (2008). Journal of Chemical Information and Modeling, 48(12), 2362.

    Article  CAS  Google Scholar 

  • Prathipati, P., & Mizuguchi, K. (2016a). Current Topics in Medicinal Chemistry, 16(9), 1009.

    Google Scholar 

  • Prathipati, P., & Mizuguchi, K. (2016b). Journal of Chemical Information and Modeling, 56(6), 974.

    Google Scholar 

  • Prathipati, P., & Saxena, A. K. (2005). Journal of Computer-Aided Molecular Design, 19(2), 93.

    Article  CAS  Google Scholar 

  • Pudil, P., Novovičová, J., & Kittler, J. (1994). Pattern Recognition Letters, 15(11), 1119.

    Article  Google Scholar 

  • Ponce, Y. M. (2017a). QuBiLs-MAS. http://tomocomd.com/qubils-mas.

  • Ponce, Y. M. (2017b). QuBiLs-MIDAS. http://tomocomd.com/qubils-midas.

  • Qiu, T., Qiu, J., Feng, J., Wu, D., Yang, Y., Tang, K., et al. (2016). Briefings in Bioinformatics, 18(1), 125.

    Google Scholar 

  • Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco, USA: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • RapidMiner, Inc. (2017). RapidMiner. https://rapidminer.com/.

  • rcdk: Interface to the CDK Libraries. https://cran.r-project.org/web/packages/rcdk/index.html.

  • Rácz, A., Bajusz, D., & Héberger, K. (2015). SAR and QSAR in Environmental Research, 26(7–9), 683.

    Article  CAS  Google Scholar 

  • Radoux, C. J., Olsson, T. S., Pitt, W. R., Groom, C. R., & Blundell, T. L. (2016). Journal of Medicinal Chemistry, 59(9), 4314.

    Article  CAS  Google Scholar 

  • Raiko, T., Valpola, H., & LeCun, Y. (2012). In Proceedings of the Fifteenth Internation Conference on Artificial Intelligence and Statistics (AISTATS). JMLR Workshop and Conference Proceedings (Vol. 22, pp. 924–932).

    Google Scholar 

  • Randic, M. (1975). Journal of the American Chemical Society, 97(23), 6609.

    Article  CAS  Google Scholar 

  • Ripley, B. D. (2017). The R project in statistical computing. https://www.stats.ox.ac.uk/pub/bdr/LTSN-R.pdf.

  • Rogers, D., & Hahn, M. (2010). Journal of Chemical Information and Modeling, 50(5), 742.

    Article  CAS  Google Scholar 

  • Rosenbaum, L., Hinselmann, G., Jahn, A., & Zell, A. (2011). Journal of Cheminformatics, 3(1), 11.

    Article  CAS  Google Scholar 

  • Rucker, C., Rucker, G., & Meringer, M. (2007). Journal of Chemical Information and Modeling, 47(6), 2345.

    Article  CAS  Google Scholar 

  • Rueda, M., Bottegoni, G., & Abagyan, R. (2009). Journal of Chemical Information and Modeling, 49(3), 716.

    Article  CAS  Google Scholar 

  • Rueda, M., Bottegoni, G., & Abagyan, R. (2010). Journal of Chemical Information and Modeling, 50(1), 186.

    Article  CAS  Google Scholar 

  • Sahigara, F., Mansouri, K., Ballabio, D., Mauri, A., Consonni, V., & Todeschini, R. (2012). Molecules, 17(5), 4791.

    Article  CAS  Google Scholar 

  • Sahigara, F., Ballabio, D., Todeschini, R., & Consonni, V. (2013). Journal of Cheminformatics, 5(1), 27.

    Article  CAS  Google Scholar 

  • Saito, K., & Nakano, R. (1988). In IEEE International Conference on Neural Networks, 1988 (pp. 255–262). IEEE.

    Google Scholar 

  • SAS Institute Inc. (2017). SAS Enterprise Miner. http://www.sas.com/en_th/software/analytics/enterprise-miner.html.

  • Saxena, A. K., & Prathipati, P. (2003). SAR and QSAR in Environmental Research, 14(5–6), 433.

    Article  CAS  Google Scholar 

  • Saxena, A. K., & Prathipati, P. (2006). SAR and QSAR in Environmental Research, 17(4), 371.

    Article  CAS  Google Scholar 

  • Schuffenhauer, A., Brown, N., Selzer, P., Ertl, P., & Jacoby, E. (2006). Journal of Chemical Information and Modeling, 46(2), 525.

    Article  CAS  Google Scholar 

  • Seebeck, B., Wagener, M., & Rarey, M. (2011). ChemMedChem, 6(9), 1630.

    Article  CAS  Google Scholar 

  • Selassie, C. D., Garg, R., Kapur, S., Kurup, A., Verma, R. P., Mekapati, S. B., et al. (2002). Chemical Reviews, 102(7), 2585.

    Article  CAS  Google Scholar 

  • Sestito, S., & Dillon, T. (1992). Proceedings of the 12th International Conference on Expert Systems and their Applications (AVIGNON’92) (pp. 645–656).

    Google Scholar 

  • Setiono, R., & Liu, H. (1995). Proceedings of the 14th International Joint Conference on Artificial Intelligence—Volume 1, IJCAI’95 (pp. 480–485). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • Setiono, R., & Liu, H. (1997). Neurocomputing, 17(1), 1.

    Article  Google Scholar 

  • Setiono, R., Leow, W. K., & Zurada, J. M. (2002). IEEE Transactions on Neural Networks, 13(3), 564.

    Article  CAS  Google Scholar 

  • Shafer, G., & Vovk, V. (2008). Journal of Machine Learning Research, 9, 371.

    Google Scholar 

  • Sheridan, R. P. (2015). Journal of Chemical Information and Modeling, 55(6), 1098.

    Article  CAS  Google Scholar 

  • Sheridan, R. P., & Kearsley, S. K. (1995). Journal of Chemical Information and Computer Sciences, 35(2), 310.

    CAS  Google Scholar 

  • Shoombuatong, W., Prachayasittikul, V., Prachayasittikul, V., & Nantasenamat, C. (2015). EXCLI Journal, 14, 452.

    Google Scholar 

  • Shoombuatong, W., Prachayasittikul, V., Anuwongcharoen, N., Songtawee, N., Monnor, T., Prachayasittikul, S., et al. (2015). Drug Design. Development and Therapy, 9, 4515.

    Google Scholar 

  • Siedlecki, W., & Sklansky, J. (1988). International Journal of Pattern Recognition and Artificial Intelligence, 2(02), 197.

    Article  Google Scholar 

  • Simeon, S., Möller, R., Almgren, D., Li, H., Phanus-umporn, C., Prachayasittikul, V., et al. (2016a). Chemometrics and Intelligent Laboratory Systems, 151, 51.

    Google Scholar 

  • Simeon, S., Spjuth, O., Lapins, M., Nabu, S., Anuwongcharoen, N., Prachayasittikul, V., et al. (2016b). PeerJ, 4, e1979.

    Google Scholar 

  • Simpson, P. K. (1990). Artificial neural system: Foundation, paradigm, application and implementations. Pennsylvania, USA: Windcrest/McGraw-Hill.

    Google Scholar 

  • Sippl, W. (2006). Molecular interaction fields (pp. 145–170). KGaA: Wiley-VCH Verlag GmbH & Co.

    Book  Google Scholar 

  • Skvortsova, M. I., Baskin, I. I., Slovokhotova, O. L., Palyulin, V. A., & Zefirov, N. S. (1993). Journal of Chemical Information and Computer Sciences, 33(4), 630.

    CAS  Google Scholar 

  • Sliwoski, G., Mendenhall, J., & Meiler, J. (2016). Journal of Computer-Aided Molecular Design, 30(3), 209.

    Article  CAS  Google Scholar 

  • Song, M., Breneman, C. M., Bi, J., Sukumar, N., Bennett, K. P., Cramer, S., et al. (2002). Journal of Chemical Information and Computer Sciences, 42(6), 1347.

    Article  CAS  Google Scholar 

  • Spjuth, O., Willighagen, E. L., Guha, R., Eklund, M., & Wikberg, J. E. (2010). Journal of Cheminformatics, 2, 5.

    Article  CAS  Google Scholar 

  • Spyrakis, F., & Cavasotto, C. N. (2015). Archives of Biochemistry and Biophysics, 583, 105.

    Article  CAS  Google Scholar 

  • Stalring, J. C., Carlsson, L. A., Almeida, P., & Boyer, S. (2011). Journal of Cheminformatics, 3, 28.

    Article  CAS  Google Scholar 

  • Standfuss, J., Edwards, P. C., D’Antona, A., Fransen, M., **e, G., Oprian, D. D., et al. (2011). Nature, 471(7340), 656.

    Article  CAS  Google Scholar 

  • Stumpfe, D., Hu, Y., Dimova, D., & Bajorath, J. (2014). Journal of Medicinal Chemistry, 57(1), 18.

    Article  CAS  Google Scholar 

  • Sushko, I., Novotarskyi, S., Krner, R., Pandey, A. K., Rupp, M., et al. (2011). Journal of Computer-Aided Molecular Design, 25(6), 533.

    Article  CAS  Google Scholar 

  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K., & Q. Weinberger (Ed.) Advances in neural information processing systems 27 (pp. 3104–3112). Curran Associates, Inc.

    Google Scholar 

  • The MathWorks, Inc. (2017a). Neural Network Toolbox. http://www.mathworks.com/products/neural-network/.

  • The MathWorks, Inc. (2017b). MATLAB. https://www.mathworks.com/products/matlab/.

  • TIBCO Software Inc. (2017). TIBCO Spotfire S+. http://spotfire.tibco.com/discover-spotfire/who-uses-spotfire/by-role/statisticians.

  • Tarca, A. L., Than, N. G., & Romero, R. (2013). Systems Biomedicine, 1(4), 217.

    Article  Google Scholar 

  • Taskinen, J., & Yliruusi, J. (2003). Advanced Drug Delivery Reviews, 55(9), 1163.

    Article  CAS  Google Scholar 

  • Thornber, C. W. (1979). Chemical Society Reviews, 8(4), 563.

    Article  CAS  Google Scholar 

  • Thorne, N., Auld, D. S., & Inglese, J. (2010). Current Opinion in Chemical Biology, 14(3), 315.

    Article  CAS  Google Scholar 

  • Thrun, S. (1993). Extracting provably correct rules from artificial neural networks. Bonn, Germany: University of Bonn.

    Google Scholar 

  • Todeschini, R., & Consonni, V. (2008). Handbook of molecular descriptors. Weinheim, Germany: Wiley-VCH Verlag GmbH.

    Google Scholar 

  • Toropov, A. A., Toropova, A. P., Benfenati, E., Leszczynska, D., & Leszczynski, J. (2010). Journal of Computational Chemistry, 31(2), 381.

    CAS  Google Scholar 

  • Toropov, A. A., Toropova, A. P., Puzyn, T., Benfenati, E., Gini, G., Leszczynska, D., et al. (2013). Chemosphere, 92(1), 31.

    Article  CAS  Google Scholar 

  • Toropova, A. P., & Toropov, A. A. (2014). European Journal of Pharmaceutical Sciences, 52, 21.

    Article  CAS  Google Scholar 

  • Toropov, A. A., & Benfenati, E. (2007a). European Journal of Medicinal Chemistry, 42(5), 606.

    Google Scholar 

  • Toropov, A. A., & Benfenati, E. (2007b). Current Drug Discovery Technologies, 4(2), 77.

    Google Scholar 

  • Tosco, P., Balle, T., & Shiri, F. (2011). Journal of Computer-Aided Molecular Design, 25(8), 777.

    Article  CAS  Google Scholar 

  • Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). QSAR and Combinatorial Science, 22(1), 69.

    Article  CAS  Google Scholar 

  • Tropsha, A. (2010). Molecular Informatics, 29(6–7), 476.

    Article  CAS  Google Scholar 

  • Venkatasubramanian, V., Chan, K., & Caruthers, J. M. (1995). Journal of Chemical Information and Computer Sciences, 35(2), 188.

    CAS  Google Scholar 

  • Verma, R. P., & Hansch, C. (2005). Bioorganic and Medicinal Chemistry, 13(15), 4597.

    Article  CAS  Google Scholar 

  • Verma, R. P., & Hansch, C. (2009). Chemical Reviews, 109(1), 213.

    Article  CAS  Google Scholar 

  • Visco, D. P., Pophale, R. S., Rintoul, M. D., & Faulon, J. L. (2002). Journal of Molecular Graphics and Modelling, 20(6), 429.

    Article  CAS  Google Scholar 

  • Walker, T., Grulke, C. M., Pozefsky, D., & Tropsha, A. (2010). Bioinformatics, 26(23), 3000.

    Article  CAS  Google Scholar 

  • Wang, L. X., & Mendel, J. M. (1992). IEEE Transactions on Systems. Man and Cybernetics: Systems, 22(6), 1414.

    Article  Google Scholar 

  • Wei, D. B., Zhang, A. Q., Han, S. K., & Wang, L. S. (2001). SAR and QSAR in Environmental Research, 12(5), 471.

    Article  CAS  Google Scholar 

  • Weis, D. C., Faulon, J. L., LeBorne, R. C., & Visco, D. P. (2005). Industrial and Engineering Chemistry, 44(23), 8883.

    Article  CAS  Google Scholar 

  • Wong, W. W., & Burkowski, F. J. (2009). Journal of Cheminformatics, 1, 4.

    Article  CAS  Google Scholar 

  • Worachartcheewan, A., Nantasenamat, C., Naenna, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2009). European Journal of Medicinal Chemistry, 44(4), 1664.

    Article  CAS  Google Scholar 

  • Worachartcheewan, A., Mandi, P., Prachayasittikul, V., Toropova, A. P., Toropov, A. A., & Nantasenamat, C. (2014). Chemometrics and Intelligent Laboratory Systems, 138, 120.

    Article  CAS  Google Scholar 

  • Worachartcheewan, A., Prachayasittikul, V., Toropova, A. P., Toropov, A. A., & Nantasenamat, C. (2015). Molecular Diversity, 19(4), 955.

    Article  CAS  Google Scholar 

  • Worth, A. P., & Cronin, M. T. (2004). Alternatives to Laboratory Animals, 32, 703.

    CAS  Google Scholar 

  • **ao, N., Cao D. S., & Xu, Q. (2017). Rcpi: Toolkit for compound-protein interaction in drug discovery. http://bioconductor.org/packages/release/bioc/html/Rcpi.html.

  • **ng, L., Glen, R. C., & Clark, R. D. (2003). Journal of Chemical Information and Computer Sciences, 43(3), 870.

    Article  CAS  Google Scholar 

  • Yager, R. R., & Filev, D. P. (1994). Journal of Intelligent & Fuzzy Systems, 2(3), 209.

    Google Scholar 

  • Yap, C. W. (2011). Journal of Computational Chemistry, 32(7), 1466.

    Article  CAS  Google Scholar 

  • Yap, C. W. (2017). PaDEL-Descriptor. http://www.yapcwsoft.com/dd/padeldescriptor.

  • Zakharov, A. V., Peach, M. L., Sitzmann, M., & Nicklaus, M. C. (2014). Journal of Chemical Information and Modeling, 54(3), 705.

    Article  CAS  Google Scholar 

  • Zell, A., Mache, N., Hubner, R., Mamier, G., Vogt, M., Döring, S., et al. (2017). SNNS: Stuttgast neural network simulator. http://www.ra.cs.uni-tuebingen.de/SNNS/.

  • Zhao, Z., Wu, H., Wang, L., Liu, Y., Knapp, S., Liu, Q., et al. (2014). ACS Chemical Biology, 9(6), 1230.

    Article  CAS  Google Scholar 

  • Zhang, Y., Su, H., Jia, T., & Chu, J. (2005). In Ho T. B., Cheung D., Liu H. (Eds.), Advances in knowledge discovery and data mining: 9th Pacific-Asia conference on knowledge discovery and data mining (pp. 61–70). Berlin/Heidelberg, Germany: Springer.

    Google Scholar 

  • Zhou, Z. H., & Chen, S. F. (2002). Journal of Research and Development, 39(4), 398.

    Google Scholar 

  • Zurada, J. M. (1992). Introduction to artificial neural systems (Vol. 8). Minnesota, USA: West Publishing Co.

    Google Scholar 

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Acknowledgements

This work is supported by a Research Career Development Grant (No. RSA5780031) to CN from the Thailand Research Fund; the New Scholar Research Grant (No. MRG5980220) to WS from the Thailand Research Fund; and the Swedish Research Links program (No. C0610701) to CN and JESW from the Swedish Research Council.

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Authors and Affiliations

  1. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Watshara Shoombuatong, Saw Simeon, Nuttapat Anuwongcharoen & Chanin Nantasenamat

  2. National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan

    Philip Prathipati

  3. Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Wiwat Owasirikul

  4. Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Apilak Worachartcheewan

  5. Department of Pharmaceutical Biosciences, BMC, Uppsala University, SE-751 24, Uppsala, Sweden

    Jarl E. S. Wikberg

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    Kunal Roy

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Shoombuatong, W. et al. (2017). Towards the Revival of Interpretable QSAR Models. In: Roy, K. (eds) Advances in QSAR Modeling. Challenges and Advances in Computational Chemistry and Physics, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-56850-8_1

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