Application of Artificial Neural Networks in Modern Drug Discovery

Artificial Neural Network for Drug Design, Delivery and Disposition

Volumn , Issue , 2016, Pages 123-139

  Mandlik, Vineetha ^a   Bejugam, Pruthvi Raj ^a   Singh, Shailza ^a  

^a NATIONAL CENTRE FOR CELL SCIENCE   (India)

Author keywords

ADME Tox; Artificial neural networks; Drug discovery; Formulation development; Pharmacophore mapping; QSAR; Virtual screening

Indexed keywords

COMPUTATIONAL CHEMISTRY; MOLECULAR GRAPHICS; NEURAL NETWORKS; PHARMACODYNAMICS;

ADME-TOX; DRUG DISCOVERY; FORMULATION DEVELOPMENT; PHARMACOPHORE MAPPING; QSAR; VIRTUAL SCREENING;

COMPLEX NETWORKS;

EID: 84967604770     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-801559-9.00006-5     Document Type: Chapter

References (31)

1. Haykin S. Neural networks 1994, Prentice-Hall.
2. Ichikawa H. Hierarchy neural networks as applied to pharmaceutical problems. Adv Drug Deliv Rev 2003, 55:1119-1147.
3. MacKay D.J.C. Bayesian interpolation. Neural Comput 1992, 4(3):415-447.
4. Street M.E., Buscema M., Smerieri A., Montanini L., Grossi E. Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction. Prog Biophys Mol Biol 2013, 113(3):433-438.
5. Adelman L.M. Molecular computations of solutions to solve combinatorial problems. Science 1994, 266(5187):1021-1024.
6. Cardelli L. Strand algebras for DNA computing. Nat Comput 2010, 10(1):407-428.
7. Kreowski H.J., Kuske S. Graph multiset transformation: a new framework for massively parallel computation inspired by DNA computing. Nat Comput 2011, 10(2):961-986.
8. Penchovsky R., Breaker R.R. Computational design and experimental validation of oligonucleotide-sensing allosteric ribozymes. Nat Biotechnol 2005, 23(11):1424-1433.
9. Penchovsky R. Computational design of allosteric ribozymes as molecular biosensors. Biotechnol Adv 2014, 10.1016/j.biotechadv.2014.05.005.
10. Penchovsky R. Computational design and biosensor applications of small molecule-sensing allosteric ribozymes. Biomacromolecules 2013, 14(4):1240-1249.
11. Penchovsky R. Engineering integrated digital circuits with allosteric ribozymes for scaling up molecular computation and diagnostics. ACS Synth Biol 2012, 1(10):471-482.
12. Ajay, Guy W.B., Mark A.M. Designing libraries with CNS activity. J Med Chem 1999, 42(24):4942-4951.
13. Victor L. Using artificial neural networks to drive virtual screening of combinatorial libraries. Drug Discov Today: BIOSILICO 2004, 2(4):149-156.
14. Devillers J. Neural networks in QSAR and drug design 1996, Academic Press.
15. Hansch C., Fujita T. p-σπ Analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 1964, 86(8):1616-1626.
16. Free S.M., Wilson J. A mathematical contribution to structure-activity studies. J Med Chem 1964, 7:395-399.
17. Baskin I.I., Palyulin V.A., Zefirov N.S. Neural networks in building QSAR models. Artificial neural networks 2009, 133-154. Humana Press.
18. Myint K.Z., Wang L., Tong Q., Xie X.Q. Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 2012, 9(10):2912-2923.
19. Zupan J., Gasteiger J. Neural networks in chemistry and drug design 1999, John Wiley & Sons, Inc.
20. Polanski J. Self-organizing neural networks for pharmacophore mapping. Adv Drug Deliv Rev 2003, 55(9):1149-1162.
21. Hecht D., Cheung M., Fogel G.B. Docking scores and QSAR using evolved neural networks for the pan-inhibition of wild-type and mutant PfDHFR by cycloguanil derivatives. Evolutionary computation 2009, CEC'09. IEEE Congress 2009, 262-269.
22. Bazeley P.S., Prithivi S., Struble C.A., Povinelli R.J., Sem D.S. Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling. J Chem information Model 2006, 46(6):2698-2708.
23. Arciniega M., Lange O.F. Improvement of virtual screening results by docking data feature analysis. J Chem Inf Model 2014.
24. Virtual screening in drug discovery 2005, CRC press. J. Alvarez, B. Shoichet (Eds.).
25. Lipinski C.A. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov Today: Technol 2004, 1(4):337-341.
26. Khan M.T.H., Fuskevåg O.M., Sylte I. Discovery of potent thermolysin inhibitors using structure based virtual screening and binding assays. J Med Chem 2009, 52(1):48-61.
27. Guerra A., Páez J.A., Campillo N.E. Artificial neural networks in ADMET modeling: prediction of blood-brain barrier permeation. QSAR Comb Sci 2008, 27(5):586-594.
28. Gao D.W., Wang P., Liang H., Peng Y.Z. A study on prediction of the bio-toxicity of substituted benzene based on artificial neural network. J Environ Sci Health Part B 2003, 38(5):571-579.
29. Formulation tools for pharmaceutical development 2013, Elsevier. J.E. Aguilar (Ed.).
30. Ibric S., Djuris J., Parojcic J., Djuric Z. Artificial neural networks in evaluation and optimization of modified release solid dosage forms. Pharmaceutics 2012, 4(4):531-550.
31. Mendyk A., Jachowicz R. Neural network as a decision support system in the development of pharmaceutical formulation-focus on solid dispersions. Expert Syst Appl 2005, 28(2):285-294.