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Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network

  • Bingsheng Chen
  • Huaijin Zhang
  • Mengshan Li
Machine Learning - Applications & Techniques in Cyber Intelligence
  • 32 Downloads

Abstract

A prediction model of pKa values of neutral and alkaline drugs based on particle swarm optimization algorithm and back propagation artificial neural network, called PSO–BP ANN, was established. PSO–BP ANN model was proposed using back propagation artificial neural network trained by particle swarm optimization algorithm, and used to predict the pKa values. The five parameters, including relative N atom number, Randic index (order 3), relative negative charge, relative negative charge surface area and maximum atomic net charge, were selected by particle swarm optimization algorithm and used as input variables of the model. The output variable in the proposed model was pKa values. The experimental results showed that the three layers (5–7–1) prediction model had a good prediction performance. The absolute mean relative error, root mean square error of prediction and square correlation coefficient were 0.5728, 0.0512 and 0.9169, respectively. The pKa values of neutral and alkaline drugs were positively correlated with the value of maximum atomic net charge, but the pKa value decreased with the increase in the other four parameters.

Keywords

pKa value Particle swarm optimization Back propagation Artificial neural network 

Notes

Acknowledgements

The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 51663001, 51463015, 61741103). The authors report no conflicts of interests in this paper.

References

  1. 1.
    Charifson PS, Walters WP (2014) Acidic and basic drugs in medicinal chemistry: a perspective. J Med Chem 57(23):9701–9717.  https://doi.org/10.1021/jm501000a CrossRefGoogle Scholar
  2. 2.
    Wang L, Zhang M, Alexov E (2016) DelPhiPKa web server: predicting pK(a) of proteins, RNAs and DNAs. Bioinformatics 32(4):614–615.  https://doi.org/10.1093/bioinformatics/btv607 CrossRefGoogle Scholar
  3. 3.
    Bochevarov AD, Watson MA, Greenwood JR (2016) Multiconformation, density functional theory-based pk(a) prediction in application to large, flexible organic molecules with diverse functional groups. J Chem Theory Comput 12(12):6001–6019.  https://doi.org/10.1021/acs.jctc.6b00805 CrossRefGoogle Scholar
  4. 4.
    Peng YH, Alexov E (2017) Computational investigation of proton transfer, pKa shifts and pH-optimum of protein-DNA and protein-RNA complexes. Proteins Struct Funct Bioinform 85(2):282–295.  https://doi.org/10.1002/prot.25221 CrossRefGoogle Scholar
  5. 5.
    Wang H, Jiang MY, Li SJ, Hse CY, Jin CD, Sun FL, Li Z (2017) Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure-activity relationship. R Soc Open Sci 4(9):170516.  https://doi.org/10.1098/rsos.170516 CrossRefGoogle Scholar
  6. 6.
    Das R, Wales DJ (2017) Machine learning landscapes and predictions for patient outcomes. R Soc Open Sci 4(7):170175.  https://doi.org/10.1098/rsos.170175 MathSciNetCrossRefGoogle Scholar
  7. 7.
    Balaji S, Revathi N (2016) A new approach for solving set covering problem using jumping particle swarm optimization method. Nat Comput 15(3):503–517.  https://doi.org/10.1007/s11047-015-9509-2 MathSciNetCrossRefGoogle Scholar
  8. 8.
    Karami H, Karimi S, Bonakdari H, Shamshirband S (2018) Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming. Neural Comput Appl 29(11):983–989.  https://doi.org/10.1007/s00521-016-2588-x CrossRefGoogle Scholar
  9. 9.
    Arefi-Oskoui S, Khataee A, Vatanpour V (2017) Modeling and optimization of NLDH/PVDF ultrafiltration nanocomposite membrane using artificial neural network-genetic algorithm hybrid. ACS Comb Sci 19(7):464–477.  https://doi.org/10.1021/acscombsci.7b00046 CrossRefGoogle Scholar
  10. 10.
    Saidi-Mehrabad M, Dehnavi-Arani S, Evazabadian F, Mahmoodian V (2015) An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput Ind Eng 86:2–13.  https://doi.org/10.1016/j.cie.2015.01.003 CrossRefGoogle Scholar
  11. 11.
    Tran DC, Wu ZJ, Wang ZL, Deng CS (2015) A novel hybrid data clustering algorithm based on artificial bee colony algorithm and K-means. Chin J Electron 24(4):694–701.  https://doi.org/10.1049/cje.2015.10.006 CrossRefGoogle Scholar
  12. 12.
    Mirabi M, Seddighi P (2018) Hybrid ant colony optimization for capacitated multiple-allocation cluster hub location problem. Artif Intell Eng Des Anal Manuf 32(1):44–58.  https://doi.org/10.1017/s0890060417000221 CrossRefGoogle Scholar
  13. 13.
    Zuvela P, Liu JJ, Macur K, Baczek T (2015) Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms. Anal Chem 87(19):9876–9883.  https://doi.org/10.1021/acs.analchem.5b02349 CrossRefGoogle Scholar
  14. 14.
    Pavao LV, Borba Costa CB, Ravagnani MASS (2017) Heat exchanger network synthesis without stream splits using parallelized and simplified simulated annealing and particle swarm optimization. Chem Eng Sci 158:96–107.  https://doi.org/10.1016/j.ces.2016.09.030 CrossRefGoogle Scholar
  15. 15.
    Niu C, Yuan YH, Guo H, Wang X, Yue TL (2018) Recognition of osmotolerant yeast spoilage in kiwi juices by near-infrared spectroscopy coupled with chemometrics and wavelength selection. RSC Adv 8(1):222–229.  https://doi.org/10.1039/c7ra12266g CrossRefGoogle Scholar
  16. 16.
    Mengshan L, Liang L, Xingyuan H, Hesheng L, Bingsheng C, Lixin G, Yan W (2017) Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory. RSC Adv 7(78):49817–49827.  https://doi.org/10.1039/C7RA09531G CrossRefGoogle Scholar
  17. 17.
    Kiran MS (2017) Particle swarm optimization with a new update mechanism. Appl Soft Comput 60:670–678.  https://doi.org/10.1016/j.asoc.2017.07.050 CrossRefGoogle Scholar
  18. 18.
    Jiang F, Xia HY, Tran QA, Ha QM, Tran NQ, Hu JK (2017) A new binary hybrid particle swarm optimization with wavelet mutation. Knowl Based Syst 130:90–101.  https://doi.org/10.1016/j.knosys.2017.03.032 CrossRefGoogle Scholar
  19. 19.
    Zuvela P, David J, Wong MW (2018) Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids. J Comput Chem 39(16):953–963.  https://doi.org/10.1002/jcc.25168 CrossRefGoogle Scholar
  20. 20.
    Li MS, Zhang HJ, Chen BS, Wu Y, Guan LX (2018) Prediction of pKa values for neutral and basic drugs based on hybrid artificial intelligence methods. Sci Rep 8(1):3991.  https://doi.org/10.1038/s41598-018-22332-7 CrossRefGoogle Scholar
  21. 21.
    Zhu QL, Lin QZ, Chen WN, Wong KC, Coello CAC, Li JQ, Chen JY, Zhang J (2017) An external archive-guided multiobjective particle swarm optimization algorithm. IEEE Trans Cybern 47(9):2794–2808.  https://doi.org/10.1109/tcyb.2017.2710133 CrossRefGoogle Scholar
  22. 22.
    Yang DX, Liu ZJ, Yi P (2017) Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization. Neural Comput Appl 28:S1245–S1264.  https://doi.org/10.1007/s00521-016-2433-2 CrossRefGoogle Scholar
  23. 23.
    Yan J, He WX, Jiang XL, Zhang ZL (2017) A novel phase performance evaluation method for particle swarm optimization algorithms using velocity-based state estimation. Appl Soft Comput 57:517–525.  https://doi.org/10.1016/j.asoc.2017.04.035 CrossRefGoogle Scholar
  24. 24.
    Shirazian S, Alibabaei M (2017) Using neural networks coupled with particle swarm optimization technique for mathematical modeling of air gap membrane distillation (AGMD) systems for desalination process. Neural Comput Appl 28(8):2099–2104.  https://doi.org/10.1007/s00521-016-2184-0 CrossRefGoogle Scholar
  25. 25.
    Date Y, Kikuchi J (2018) Application of a deep neural network to metabolomics studies and its performance in determining important variables. Anal Chem 90(3):1805–1810.  https://doi.org/10.1021/acs.analchem.7b03795 CrossRefGoogle Scholar
  26. 26.
    Chen J, Hu Q, Xue X, Ha M, Ma L (2017) Support function machine for set-based classification with application to water quality evaluation. Inf Sci 388:48–61.  https://doi.org/10.1016/j.ins.2017.01.001 MathSciNetCrossRefGoogle Scholar
  27. 27.
    Koutsoukas A, Monaghan KJ, Li XL, Huan J (2017) Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J Cheminform.  https://doi.org/10.1186/s13321-017-0226-y CrossRefGoogle Scholar
  28. 28.
    Li L, Chakravorty A, Alexov E (2017) DelPhiForce, a tool for electrostatic force calculations: applications to macromolecular binding. J Comput Chem 38(9):584–593.  https://doi.org/10.1002/jcc.24715 CrossRefGoogle Scholar
  29. 29.
    Dardonville C, Caine BA, Navarro de la Fuente M, Martin Herranz G, Corrales Mariblanca B, Popelier PLA (2017) Substituent effects on the basicity (pK(a)) of aryl guanidines and 2-(arylimino) imidazolidines: correlations of pH-metric and UV-metric values with predictions from gas-phase ab initio bond lengths. New J Chem 41(19):11016–11028.  https://doi.org/10.1039/c7nj02497e CrossRefGoogle Scholar
  30. 30.
    Heidarzadeh N (2017) A practical low-cost model for prediction of the groundwater quality using artificial neural networks. J Water Supply Res Technol AQUA 66(2):86–95CrossRefGoogle Scholar
  31. 31.
    Han S, Ko Y, Kim J, Hong T (2018) Housing market trend forecasts through statistical comparisons based on big data analytic methods. J Manag Eng 34(2):04017054CrossRefGoogle Scholar
  32. 32.
    Hasanloei MAV, Sheikhpour R, Sarram MA, Sheikhpour E, Sharifi H (2018) A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities. J Comput Aided Mol Des 32(2):375–384.  https://doi.org/10.1007/s10822-017-0094-6 CrossRefGoogle Scholar
  33. 33.
    Wang L, You ZH, Chen X, Xia SX, Liu F, Yan X, Zhou Y, Song KJ (2018) A computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J Comput Biol 25(3):361–373.  https://doi.org/10.1089/cmb.2017.0135 MathSciNetCrossRefGoogle Scholar
  34. 34.
    Zeinali Y, Story BA (2017) Competitive probabilistic neural network. Integr Comput Aided Eng 24(2):105–118.  https://doi.org/10.3233/ica-170540 CrossRefGoogle Scholar
  35. 35.
    Bui KTT, Bui DT, Zou JG, Doan CV, Revhaug I (2018) A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput Appl 29(12):1495–1506.  https://doi.org/10.1007/s00521-016-2666-0 CrossRefGoogle Scholar
  36. 36.
    Hase F, Kreisbeck C, Aspuru-Guzik A (2017) Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. Chem Sci 8(12):8419–8426.  https://doi.org/10.1039/c7sc03542j CrossRefGoogle Scholar
  37. 37.
    Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput Chem 38(16):1291–1307.  https://doi.org/10.1002/jcc.24764 CrossRefGoogle Scholar
  38. 38.
    Polanski J, Walczak B (2000) The comparative molecular surface analysis (COMSA): a novel tool for molecular design. Comput Chem 24(5):615–625.  https://doi.org/10.1016/S0097-8485(00)00064-4 CrossRefGoogle Scholar
  39. 39.
    Luan F, Ma WP, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT (2005) Prediction of pK(a) for neutral and basic drugs based on radial basis function neural networks and the heuristic method. Pharm Res 22(9):1454–1460.  https://doi.org/10.1007/s11095-005-6246-8 CrossRefGoogle Scholar
  40. 40.
    Luan F, Xu X, Liu HT, Cordeiro M (2013) Review of quantitative structure-activity/property relationship studies of dyes: recent advances and perspectives. Color Technol 129(3):173–186.  https://doi.org/10.1111/cote.12027 CrossRefGoogle Scholar
  41. 41.
    Marjani A, Shirazian S, Asadollahzadeh M (2018) Topology optimization of neural networks based on a coupled genetic algorithm and particle swarm optimization techniques (c-GA-PSO-NN). Neural Comput Appl 29(11):1073–1076.  https://doi.org/10.1007/s00521-016-2619-7 CrossRefGoogle Scholar
  42. 42.
    Wang H, Sun H, Li CH, Rahnamayan S, Pan JS (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135.  https://doi.org/10.1016/j.ins.2012.10.012 MathSciNetCrossRefGoogle Scholar
  43. 43.
    Martinez-Vargas A, Andrade AG (2013) Comparing particle swarm optimization variants for a cognitive radio network. Appl Soft Comput 13(2):1222–1234.  https://doi.org/10.1016/j.asoc.2012.10.016 CrossRefGoogle Scholar
  44. 44.
    Xiao Y, Xiao J, Lu FB, Wang SY (2013) Ensemble ANNs-PSO-GA approach for day-ahead stock e-exchange prices forecasting. Int J Comput Intell Syst 6(1):96–114.  https://doi.org/10.1080/18756891.2013.756227 CrossRefGoogle Scholar
  45. 45.
    Wang HS, Wang YN, Wang YC (2013) Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Syst Appl 40(2):418–428.  https://doi.org/10.1016/j.eswa.2012.01.166 CrossRefGoogle Scholar
  46. 46.
    Sermpinis G, Theofilatos K, Karathanasopoulos A, Georgopoulos EF, Dunis C (2013) Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization. Eur J Oper Res 225(3):528–540.  https://doi.org/10.1016/j.ejor.2012.10.020 MathSciNetCrossRefzbMATHGoogle Scholar
  47. 47.
    Li M, Huang X, Liu H, Liu B, Wu Y, Wang L (2015) Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means. RSC Adv 5(56):45520–45527.  https://doi.org/10.1039/c5ra07129a CrossRefGoogle Scholar
  48. 48.
    Li M, Wu W, Chen B, Wu Y, Huang X (2017) Solubility prediction of gases in polymers based on an artificial neural network: a review. RSC Adv 7(56):35274–35282.  https://doi.org/10.1039/c7ra04200k CrossRefGoogle Scholar
  49. 49.
    Zhang L, Wang FL, Sun T, Xu B (2018) A constrained optimization method based on BP neural network. Neural Comput Appl 29(2):413–421.  https://doi.org/10.1007/s00521-016-2455-9 CrossRefGoogle Scholar
  50. 50.
    Balabin RM, Smirnov SV (2011) Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. Anal Chim Acta 692(1–2):63–72.  https://doi.org/10.1016/j.aca.2011.03.006 CrossRefGoogle Scholar
  51. 51.
    Hu WB, Wang H, Qiu ZY, Nie C, Yan LP (2018) A quantum particle swarm optimization driven urban traffic light scheduling model. Neural Comput Appl 29(3):901–911.  https://doi.org/10.1007/s00521-016-2508-0 CrossRefGoogle Scholar
  52. 52.
    Kalaiarasi N, Dash SS, Padmanaban S, Paramasivam S, Morati PK (2018) Maximum power point tracking implementation by dspace controller integrated through z-source inverter using particle swarm optimization technique for photovoltaic applications. Appl Sci Basel.  https://doi.org/10.3390/app8010145 CrossRefGoogle Scholar
  53. 53.
    Das GS (2017) Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization. Neural Comput Appl 28:S539–S549.  https://doi.org/10.1007/s00521-016-2367-8 CrossRefGoogle Scholar
  54. 54.
    Javidrad F, Nazari M (2017) A new hybrid particle swarm and simulated annealing stochastic optimization method. Appl Soft Comput 60:634–654.  https://doi.org/10.1016/j.asoc.2017.07.023 CrossRefGoogle Scholar
  55. 55.
    Eckert F, Klamt A (2006) Accurate prediction of basicity in aqueous solution with COSMO-RS. J Comput Chem 27(1):11–19.  https://doi.org/10.1002/jcc.20309 CrossRefGoogle Scholar
  56. 56.
    Kromann JC, Larsen F, Moustafa H, Jensen JH (2016) Prediction of pKa values using the PM6 semiempirical method. Peerj.  https://doi.org/10.7717/peerj.2335 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Physics and Electronic InformationGannan Normal UniversityGanzhouChina

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