• Ankith M. Department of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Tamilnadu, India
  • Surya Teja S. P. Department of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Tamilnadu, India
  • Damodharan N. Department of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Tamilnadu, India


Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also.

Keywords: Artificial intelligence, QSAR, Optimization, Preformulation, Toxicity Prediction, Drug discovery


1. Wesolowski M, Suchacz B. Artificial neural networks: theoretical background and pharmaceutical applications: a review J. AOAC Int 2012;95:652-68.
2. Carpenter H, M WC. Understanding neural network approximations and polynomial approximations helps neural network performance. AI Expert 1995;2:31-3.
3. Agatonovic Kustrin S, Beresford R. Basic concepts of the artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 2000;22:717-27.
4. Ferrari S, Stengel RF. Smooth function approximation using neural networks. IEEE Trans Neural Netw (a publication of the IEEE Neural Networks Council) 2005;16:24-8.
5. Erb RJ. Introduction to backpropagation neural network computation. Pharm Res 1993;10:165-70.
6. Liu F, Er MJ. A novel efficient learning algorithm for self-generating a fuzzy neural network with applications. Int J Neural Syst 2012;22:21-35.
7. Bourquin J, Schmidli H, Van Hoogevest P, Leuenberger H. Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development. Pharm Dev Technol 1997;2:95-109.
8. Chen CP, Zhang CY. Data-intensive applications, challenges, techniques, and technologies: a survey on big data. Information Sciences. Asian J Pharm Clin Res 2014;275:314-47.
9. Holmstrom L, Koistinen P. Using additive noise in backpropagation training. IEEE Trans Neural Netw 1992;3:24-38.
10. Pukrittayakamee A, Hagan M, Raff L, Bukkapatnam ST, Komanduri R. Practical training framework for fitting a function and its derivatives. Neural Netw IEEE Trans 2011;22:936-47.
11. Wu J, Mei J, Wen S, Liao S, Chen J, Shen Y. Self-adaptive genetic algorithm-artificial neural network algorithms with leave-one-out cross-validation for descriptor selection in QSAR study. J Comput Chem 2010;31:1954-68.
12. Bouleimen K, Lecocq H. A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode versions. Eur J Oper Res 2003;148:268-81.
13. Inden B, Jin Y, Haschke R, Ritter H. Evolving neural fields for problems with large input and output spaces. Neural Net 2012;28:24-39.
14. Jadid MN, Fairbairn DR. Neural-network applications in predicting moment-curvature parameters from experimental data. Eng Appl Artif Intell 1996;9:309-19.
15. Livingstone DR, Manallack DT, Tetko IV. Data modeling with neural networks: advantages and limitations. J Comput Aided Mol Des 1997;11:135-42.
16. Olier I, Sadawi N, Bickerton GR, Vanschoren J, Grosan C, Soldatova L, et al. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Mach Learn 2018;107:285-311.
17. Gawad J, Bonde C. Artificial intelligence: future of medicine and healthcare. Asian J Pharm Clin Res 2017;11:355-60.
18. Hu L, Chen G, Chau RM. A neural network-based drug discovery approach and its application for designing aldose reductase inhibitors. J Mol Graph Model 2003;24:244-53.
19. Gao DW, Wang P, Liang H, Peng YZ. A study on prediction of the bio-toxicity of substituted benzene based on the artificial neural network. J Environ Sci Health B 2003;38:571-9.
20. Jaen Oltra J, Salabert Salvador MT, Garcia March FJ, Perez Gimenez F, Tomas-Vert F. Artificial neural network applied to prediction of fluoroquinolone antibacterial activity by topological methods. J Med Chem 2000;43:1143-8.
21. Eros D, Kovesdi I, Orfi L, Takacs Novak K, Acsady G, Keri G. Reliability of logP predictions based on calculated molecular descriptors: a critical review. Curr Med Chem 2000;9:1819-29.
22. Yan A, Chen X, Zhang R, Liu M, Hu Z, Fan BT. Predicting the standard enthalpy (deltaH0f) and entropy (S0) of alkanes by artificial neural networks. SAR. QSAR. Environ Res 2000; 11:235-44.
23. Liu S. Neural network-topological indices approach to the prediction of properties of the alkene. J Chem Inform Computer Sci 1997;37:1146-51.
24. Kirkpatrick S, Gelatt CDJR, Vecchi MP. Optimization by simulated annealing. Science 1983;220:671-80.
25. Glass BD, Agatonovic Kustrin S, Wisch MH. Artificial neural networks to optimize formulation components of a fixed dose combination of rifampicin, isoniazid, and pyrazinamide in a microemulsion. Curr Drug Discovery Technol 2005;2:195-201.
26. Takahara J. Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations. J Controlled Release 1997;11:11-20.
27. Ibric S, Jovanovic M, Djuric Z, Parojcic J, Petrovic SD, Solomun L, et al. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance. AAPS Pharm Sci Tech 2003;4:E9.
28. Zupancic Bozic D, Vrecer F, Kozjek F. Optimization of diclofenac sodium dissolution from sustained release formulations using an artificial neural network. Eur J Pharm Sci 1997;5:163-9.
29. Aoyama T, Suzuki Y, Ichikawa H. Neural networks applied to quantitative structure-activity relationship analysis. J Med Chem 1990;33:2583-90.
30. Agatonovic Kustrin S, Zecevic M, Zivanovic L. Use of ANN modeling in structure-retention relationships of diuretics in RP-HPLC. J Pharm Biomed Anal 1999;21:95-103.
31. Myint KZ, Wang L, Tong Q, Xie XQ. Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 2012;9:2912-23.
32. Nirouei M, Ghasemi G, Abdolmaleki P, Tavakoli A, Shariati S. Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment. Indian J Biochem Biophys 2012;49:202-10.
33. Takayama K, Fujikawa M, Nagai T. Artificial neural network as a novel method to optimize pharmaceutical formulations. Pharm Res 2011;16:1-6.
34. Nestorov IS, Hadjitodorov ST, Petrov I, Rowland M. Empirical versus mechanistic modeling: comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates. AAPS Pharm Sci 1999;1:17.
35. Vaithiyalingam S, Khan MA. Optimization and characterization of controlled release multi-particulate beads formulated with a customized cellulose acetate butyrate dispersion. Int J Pharm 2002;234:179-93.
36. Erturk MD, Saban MT, Novic M, Minovski N. Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols. J Mol Graph Model 2012;38:90-100.
37. Yaffe D, Cohen Y, Espinosa G, Arenas A, Giralt F. Fuzzy ARTMAP and back-propagation neural networks based quantitative structure-property relationships (QSPRs) for octanol-water partition coefficient of organic compounds. J Chem Inform Computer Sci 2002;42:162-83.
38. Myint KZ, Wang L, Tong Q, Xie XQ. Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 2012;9:2912-23.
39. Wu CH. Artificial neural networks for molecular sequence analysis. J Computer Chem 1997;21:237-56.
40. Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK. A dual neural network ensemble approach for multiclass brain tumor classification. Int J Numer Method Biomed Eng 2012;28:1107-20.
41. Luque NR, Garrido JA, Ralli J, Laredo JJ, Ros E. From sensors to spikes: evolving receptive fields to enhance sensorimotor information in a robot arm. Int J Neural Syst 2012; 22:12500-13.
42. Gobburu JV, Shelver WH. Quantitative structure-pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks. J Pharm Sci 1995;84:862-65.
43. Samarasinghe S. Neural networks for applied sciences and engineering; auerbach publications: New York, NY, USA; 2006.
44. Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 2000;4:3–31.
45. Dearden JC, Hewitt M. QSAR modeling of bioconcentration factor using hydrophobicity, hydrogen bonding, and topological descriptors. SAR. QSAR. Environ Res 2010;21:6671-80.
46. Chen Y, McCall TW, Baichwal AR, Meyer MC. The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage forms. J Controlled Release 1999;59:33-41.
47. Gobburu JV, Chen EP. Artificial neural networks as a novel approach to integrated pharmacokinetic-pharmacodynamic analysis. J Pharm Sci 1996;85:505-10.
48. Ritschel WA, Akileswaran R, Hussain AS. Application of neural networks for the prediction of human pharmacokinetic parameters. Methods Find Exp Clin Pharmacol 1994;19:629-43.
49. Brier ME, Zurada JM, Aronoff GR. Neural network predicted peak and trough gentamicin concentrations. J Pharm Res 1995;12:406–12.
50. Haidar SH, Johnson SB, Fossler MJ, Hussain AS. Modeling the pharmacokinetics and pharmacodynamics of a unique oral hypoglycemic agent using neural networks. J Pharm Res 2002;19:87-91.
51. Gaweda AE, Jacobs AA, Brier ME, Zurada JM. Pharmacodynamic population analysis in chronic renal failure using artificial neural networks–a comparative study. Neural Net 2003; 16:841–5.
52. Chow HH, Tolle KM, Roe DJ, Elsberry V, Chen H. Application of neural networks to population pharmacokinetic data analysis. J Pharm Sci 1997;86:840-5.
53. Hussain AS, Johnson RD, Vachharajani NN, Ritschel WA. Feasibility of developing a neural network for prediction of human pharmacokinetic parameters from animal data. J Pharm Res 1993;10:466-9.
54. Veng Pedersen P, Modi NB. Neural networks in pharmacodynamic modeling. Is the current modeling practice of complex kinetic system. J Pharmacokinet Biopharm 1992; 20:397–412.
55. Leung FHF, Lam HK, Ling SH, Tam PKS. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE trans Neural Net 2003;14:79-88.
56. Andreas Breindl BB, Timothy Clark, Robert C Glen. Prediction of the n-Octanol/water partition coefficient, logp, using a combination of semiempirical calculations and a neural network. J Mol Model 1997;3:142-55.
57. Gobburu JV, Chen EP. Artificial neural networks as a novel approach to integrated pharmacokinetic-pharmacodynamic analysis. J Pharm Sci 1996;85:505-10.
58. Hasani M, Moloudi M, Emami M. Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models. Anal Biochem 2007;370:68-76.
59. Baczek T, Bucinski A, Ivanov AR, Kaliszan R. Artificial neural network analysis for evaluation of peptide MS/MS spectra in proteomics. Anal Biochem 2004;76:1726-32.
60. Murvai J, Vlahovicek K, Szepesvari C, Pongor S. Prediction of protein functional domains from sequences using artificial neural networks. Genome Res 2001;11:1410-7.
61. Zhao W, Davis CE. A modified artificial immune system based pattern recognition approach--an application to clinical diagnostics. Artif Intell Med 2011;52:1-9.
62. Torrent M, Andreu D, Mogues V, Boix E. Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PloS One 2011;6:16968.
63. Koba M. Application of artificial neural networks for the prediction of antitumor activity of a series of acridinone derivatives. J Med Chem 2012;8:309-19.
64. Giordano A, Giuliano M, Laurentiis DE, Eleuteri A, Iorio F, Tagliaferri R, et al. Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients. Breast Cancer Res Treat 2011;129:451-8.
65. Bahari MH, Mahmoudi M, Azemi A, Mirsalehi MM, Khademi M. Early diagnosis of systemic lupus erythematosus using ANN models of ds DNA binding antibody sequence data. Bioinform 2010;5:58-61.
66. Pedersen SM, Jorgensen JS, Pedersen JB. Use of neural networks to diagnose acute myocardial infarction. I. A clinical application. Clin Chem 1996;42:613-7.
67. Baxt WG. Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction. Anal Emerg Med 1992;21:1439-44.
68. Leoncini G, Sacchi G, Viazzi F, Ravera M, Parodi D, Ratto E, Vettoretti S, TomolilloC,Deferrari G, and Pontremoli R. Microalbuminuria identifies overall cardiovascular risk in essential hypertension: an artificial neural network-based approach. J. Hypertens2002;20:1315-21.
69. Mello G, Parretti E, Ognibene A, Mecacci F, Cioni R, Scarselli G, et al. Prediction of the development of pregnancy-induced hypertensive disorders in high-risk pregnant women by artificial neural networks. Clin Chem Laboratory Med 2001;39:801-5.
70. Deng X, Li K, Liu S. Preliminary study on application of artificial neural network to the diagnosis of Alzheimer's disease with magnetic resonance imaging. Chin Med J 1999;112:232-37.
71. Sardari S, Sardari D. Applications of the artificial neural network in AIDS research and therapy. Curr Pharm Des 2002;8:659-70.
72. Schmid P, Wischnewsky MB, Sezer O, Bohm R, Possinger K. Prediction of response to hormonal treatment in metastatic breast cancer. Oncology 2002;63:309-16.
73. Jaen Oltra J, Salabert Salvador MT, Garcia March FJ, Perez Gimenez F, Tomas-Vert F. Artificial neural network applied to prediction of fluoroquinolone antibacterial activity by topological methods. J Med Chem 2000;43:1143-8.
74. Caramella C, Ferrari F, Bonferoni MC, Sangalli ME, De Bernardi, Di Valserra, et al. In vitro/in vivo correlation of prolonged release dosage forms containing diltiazem HCl. Biopharm Drug Dispos 1993;14:143-60.
75. Dowell, Hussain JA, Devane D, Young JD. Artificial neural networks applied to the in vitro–in vivo correlation of extended-release formulations: initial trials and experience. J Pharm Sci 1999;88:154–60.
76. Parojcic J, Ibric S, Duric Z, Jovanovic M, Corrigan OI. An investigation into the usefulness of generalized regression neural network analysis in the development of level a in vitro–in vivo correlation. Eur J Pharm Sci 2007;30:264–72.
77. Munishpuri, Yashwant P, Vijay Kumar S, Tipparaji M. Artificial neural network as helping tool for drug formulation USA; 2016. p. 270-2.
78. Torii K, Matsumoto K, Nakakoji K, Takada Y, Shima K. An Environment for computer-aided empirical software engineering. IEEE Trans Neural Netw 1999;25:474-92.
79. Alfredo W, Michael A, Amanda A. The neural simulation language: a system for brain modeling. The MIT Press; 2002.
103 Views | 54 Downloads
How to Cite
M., A., P., S. T., & N., D. (2018). ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY. International Journal of Applied Pharmaceutics, 10(5), 28-33.
Review Article(s)