Development and rigorous validation of antimalarial predictive models using machine learning approaches

References

• World Health Organization, Fact Sheet: World Malaria Report 2016, WHO, 2016; available at http://www.who.int/malaria/media/world-malaria-report-2016/en/.
   Google Scholar
• W. Peters, Drug resistance in malaria parasites of animals and man, Adv. Parasitol. 41 (1998), pp. 1–62. doi:10.1016/s0065-308x(08)60421-2.
   PubMed Web of Science ®Google Scholar
• B. Blasco, D. Leroy, and D.A. Fidock, Antimalarial drug resistance: Linking Plasmodium falciparum parasite biology to the clinic, Nat. Med. 23 (2017), pp. 917–928. doi:10.1038/nm.4381.
   PubMed Web of Science ®Google Scholar
• A.M. Dondorp, F. Nosten, P. Yi, D. Das, A.P. Phyo, J. Tarning, K.M. Lwin, F. Ariey, W. Hanpithakpong, S.J. Lee, P. Ringwald, K. Silamut, M. Imwong, K. Chotivanich, P. Lim, T. Herdman, S.S. An, S. Yeung, P. Singhasivanon, N.P. Day, N. Lindegardh, D. Socheat, and N.L. White, Artemisinin resistance in Plasmodium falciparum malaria, N. Engl. J. Med. 361 (2009), pp. 455–467. doi:10.1056/NEJMoa0808859.
   PubMed Web of Science ®Google Scholar
• Y. Lubell, A. Dondorp, P.L. Guérin, T. Drake, S. Meek, E. Ashley, N.P. Day, N.J. White, and L.J. White, Artemisinin resistance–modelling the potential human and economic costs, Malar. J. 13 (2014), pp. 452. doi:10.1186/1475-2875-13-452.
   PubMed Web of Science ®Google Scholar
• L. Paloque, A.P. Ramadani, O. Mercereau-Puijalon, J.M. Augereau, and F. Benoit-Vical, Plasmodium falciparum: Multifaceted resistance to artemisinins, Malar. J. 15 (2016), pp. 149. doi:10.1186/s12936-016-1206-9.
   PubMedGoogle Scholar
• J.R. Broach and J. Thorner, High-throughput screening for drug discovery, Nature 384 (1996), pp. 14–16. doi:10.1038/384014a0.
   PubMed Web of Science ®Google Scholar
• P. Szymański, M. Markowicz, and E. Mikiciuk-Olasik, Adaptation of high-throughput screening in drug discovery—toxicological screening tests, Int. J. Mol. Sci. 13 (2012), pp. 427–452. doi:10.3390/ijms13010427.
   PubMed Web of Science ®Google Scholar
• T. Zhu, S. Cao, P.C. Su, R. Patel, D. Shah, H.B. Chokshi, R. Szukala, M.E. Johnson, and K.E. Hevener, Hit identification and optimization in virtual screening: Practical recommendations based on a critical literature analysis, J. Med. Chem. 56 (2013), pp. 6560–6572. doi:10.1021/jm301916b.
   PubMed Web of Science ®Google Scholar
• P.D. Lyne, Structure-based virtual screening: An overview, Drug. Discov. Today 7 (2002), pp. 1047–1055.
   PubMed Web of Science ®Google Scholar
• A. Gaulton, L.J. Bellis, A.P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, and J.P. Overington, ChEMBL: A large-scale bioactivity database for drug discovery, Nucleic Acids Res. 40 (2012), pp. D1100–7. doi:10.1093/nar/gkr777.
   PubMed Web of Science ®Google Scholar
• R. Guha and E. Willighagen, A survey of quantitative descriptions of molecular structure, Curr. Top. Med. Chem. 12 (2012), pp. 1946–1956.
   PubMed Web of Science ®Google Scholar
• C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474. doi:10.1002/jcc.21707.
   PubMed Web of Science ®Google Scholar
• I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learn. 46 (2002), pp. 389–422. doi:10.1023/A:1012487302797.
   Web of Science ®Google Scholar
• T. Kohonen, Essentials of the self-organizing map, Neural. Netw. 37 (2013), pp. 52–65. doi:10.1016/j.neunet.2012.09.018.
   PubMed Web of Science ®Google Scholar
• T. Kohonen, Self-organized formation of topologically correct feature maps, Biol. Cybern. 43 (1982), pp. 59–69. doi:10.1007/BF00337288.
   Web of Science ®Google Scholar
• M.K. Warmuth, J. Liao, G. Rätsch, M. Mathieson, S. Putta, and C. Lemmen, Active learning with support vector machines in the drug discovery process, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 667–673. doi:10.1021/ci025620t.
   PubMedGoogle Scholar
• L. Breiman, Random forests, Mach. Learn. 45 (2001), pp. 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, in Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, CA, August 13–17, 2016, pp. 785–794.
   Google Scholar
• Z. Voulgaris and G.D. Magoulas, Extensions of the k nearest neighbour methods for classification problems, in Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications (AIA ‘08), ACTA Press, Anaheim, CA, February, 2008, pp. 23–28,
   Google Scholar
• T. Mitchell, Machine Learning, McGraw Hill, New York, 1997.
   Google Scholar
• P.A. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern. Recogn. 30 (1997), pp. 1145–1159. doi:10.1016/S0031-3203(96)00142-2.
   Web of Science ®Google Scholar
• G.J. Filion, The signed Kolmogorov-Smirnov test: Why it should not be used, Gigascience 4 (2015), pp. 9. doi:10.1186/s13742-015-0048-7.
   PubMed Web of Science ®Google Scholar
• C. Rücker, G. Rücker, and M. Meringer, y-Randomization and its variants in QSPR/QSAR, J. Chem. Inf. Model. 47 (2007), pp. 2345–2357. doi:10.1021/ci700157b.
   PubMed Web of Science ®Google Scholar
• O. Nicolotti, D. Gadaleta, G.F. Mangiatordi, M. Catto, and A. Carotti, Applicability domain for QSAR models: Where theory meets reality, Int. J. Quant. Struct.-Prop. Relat. 1 (2016), pp. 45–63. doi:10.4018/IJQSPR.2016010102.
   Google Scholar
• E. Bura and J.L. Gastwirth, The binary regression quantile plot: Assessing the importance of predictors in binary regression visually, Biom. J. 43 (2001), pp. 5–21. doi:10.1002/1521-4036(200102)43:1<5::AID-BIMJ5>3.0.CO;2-6.
   Web of Science ®Google Scholar
• M.C. Sachs and X.H. Zhou, Partial summary measures of the predictiveness curve, Biom. J. 55 (2013), pp. 589–602. doi:10.1002/bimj.201200146.
   PubMed Web of Science ®Google Scholar
• C. Empereur-Mot, H. Guillemain, A. Latouche, J.F. Zagury, V. Viallon, and M. Montes, Predictiveness curves in virtual screening, J. Cheminform. 7 (2015), pp. 52. doi:10.1186/s13321-015-0100-8.
   PubMed Web of Science ®Google Scholar
• A.J. Vickers, B. Van Calster, and E.W. Steyerber, Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests, BMJ 352 (2016), pp. i6. doi:10.1136/bmj.i1717.
   PubMedGoogle Scholar
• G. Landrum, RDKit documentation, Release 3 (2013), pp. 1–79.
   Google Scholar
• L.B. Akella and D. De Caprio, Cheminformatics approaches to analyze diversity in compound screening libraries, Curr. Opin. Chem. Biol. 14 (2010), pp. 325–330. doi:10.1016/j.cbpa.2010.03.017.
   PubMed Web of Science ®Google Scholar
• J.J. Perez, Managing molecular diversity, Chem. Soc. Rev. 34 (2005), pp. 143–152. doi:10.1039/b209064n.
   PubMed Web of Science ®Google Scholar
• X.G. Yang, D. Chen, M. Wang, Y. Xue, and Y.Z. Chen, Prediction of antibacterial compounds by machine learning approaches, J. Comput. Chem. 30 (2009), pp. 1202–1211. doi:10.1002/jcc.21148.
   PubMed Web of Science ®Google Scholar
• S. Sarkar, A.A. Siddiqui, S.J. Saha, R. De, S. Mazumder, C. Banerjee, M.S. Iqbal, S. Nag, S. Adhikari, and U. Bandyopadhyay, Antimalarial activity of small-molecule benzothiazole hydrazones, Antimicrob. Agents Chemother. 60 (2016), pp. 4217–4228. doi:10.1128/AAC.01575-15.
   PubMed Web of Science ®Google Scholar
• K. Roy, D.K. Pal, A. Saha, and C. Sengupta, QSAR with electrotopological state atom index: Part II ± antimalarial activity of dihydroqinghaosu derivatives, Indian J. Chem. 40 (2001), pp. 587–595.
   Google Scholar
• X. Hou, X. Chen, M. Zhang, and A. Yan, QSAR study on the antimalarial activity of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors, SAR QSAR Environ. Res. 27 (2016), pp. 101–124. doi:10.1080/1062936X.2015.1134652.
   PubMed Web of Science ®Google Scholar
• G.M. Keseru and G.M. Makara, Hit discovery and hit-to-lead approaches, Drug. Discov. Today 11 (2006), pp. 741–748. doi:10.1016/j.drudis.2006.06.016.
   PubMed Web of Science ®Google Scholar
• J.L. Medina-Franco, Activity cliffs: Facts or artifacts? Chem. Biol. Drug. Des. 81 (2013), pp. 553–556. doi:10.1111/cbdd.12115.
   PubMed Web of Science ®Google Scholar
• W. Klingspohn, M. Mathea, L.A. Ter, N. Heinrich, and K. Baumann, Efficiency of different measures for defining the applicability domain of classification models, J. Cheminform. 9 (2017), pp. 44. doi:10.1186/s13321-017-0230-2.
   PubMed Web of Science ®Google Scholar
• N. Aniceto, A.A. Freitas, A. Bender, and T. Ghafourian, A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: Reliability-density neighbourhood, J. Cheminform. 8 (2016), pp. 69. doi:10.1186/s13321-016-0182-y.
   Web of Science ®Google Scholar
• N. Fechner, G. Hinselmann, A. Jahn, and A. Zell, Kernel-based estimation of the applicability domain of QSAR models, J. Cheminform. 2 (2010), pp. 38. doi:10.1186/1758-2946-2-S1-P38.
   Web of Science ®Google Scholar
• D.S. Wishart, Y.D. Feunang, A.C. Guo, E.J. Lo, A. Marcu, J.R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, N. Assempour, I. Iynkkaran, Y. Liu, A. Maciejewski, N. Gale, A. Wilson, L. Chin, R. Cummings, D. Le, A. Pon, C. Knox, and M. Wilson, DrugBank 5.0: A major update to the DrugBank database for 2018, Nucleic. Acids. Res. 46 (2018), pp. D1074- D1082. doi:10.1093/nar/gkx1037.
   PubMed Web of Science ®Google Scholar
• P.F.J. Lipiński and P. Szurmak, SCRAMBLE’N’GAMBLE: A tool for fast and facile generation of random data for statistical evaluation of QSAR models, Chem. Zvesti. 71 (2017), pp. 2217–2232. doi:10.1007/s11696-017-0215-7.
   PubMed Web of Science ®Google Scholar
• D.A. Kumar, F. Mobeen, and A.U. Khan, Development of ligand and structure-based classification models to design novel inhibitors against antibiotic hydrolyzing enzymes: Integration of web server, J. Biomol. Struct. Dyn. 36 (2018), pp. 2966–2975. doi:10.1080/07391102.2017.1373034.
   PubMed Web of Science ®Google Scholar
• M.P. Naein and G.F. Cooper, Binary classifier calibration using an ensemble of piecewise linear regression models, Knowl. Inf. Syst. 54 (2017), pp. 151–170.
   PubMed Web of Science ®Google Scholar
• Y. Huang, P.M. Sullivan, and Z. Feng, Evaluating the predictiveness of a continuous marker, Biometrics 63 (2007), pp. 1181–1188. doi:10.1111/j.1541-0420.2007.00814.x.
   PubMed Web of Science ®Google Scholar
• M.S. Pepe, Z. Feng, Y. Huang, G. Longton, R. Prentice, I.M. Thompson, and Y. Zheng, Integrating the predictiveness of a marker with its performance as a classifier, Am. J. Epidemiol. 167 (2008), pp. 362–368. doi:10.1093/aje/kwm305.
   PubMed Web of Science ®Google Scholar
• S.A. Egieyeh, J. Syce, S.F. Malan, and A. Christoffels, Prioritization of antimalarial hits from nature: Chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered antimalarial drugs, Malar. J. 15 (2016), pp. 50. doi:10.1186/s12936-016-1087-y.
   PubMed Web of Science ®Google Scholar
• S. Egieyeh, J. Syce, S.F. Malan, and A. Christoffels, Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach, PLoS ONE 13 (2018), pp. e0204644. doi:10.1371/journal.pone.0204644.
   PubMed Web of Science ®Google Scholar
• N. Mahmoudi, J.V. de Julián-Ortiz, L. Ciceron, J. Gálvez, D. Mazier, M. Danis, F. Derouin, and R. García-Domenech, Identification of new antimalarial drugs by linear discriminant analysis and topological virtual screening, J. Antimicrob. Chemother. 57 (2006), pp. 489–497. doi:10.1093/jac/dki470.
   PubMed Web of Science ®Google Scholar