Development and uses of artificial intelligence in chemistry

Reviews in Computational Chemistry

Volumn 25, Issue , 2007, Pages 349-389

  Cartwright, Hugh ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 66149091150     PISSN: 10693599     EISSN: None     Source Type: Book Series    
DOI: None     Document Type: Article

References (63)

1. R. Judson, in Reviews in Computational Chemistry, Vol. 10, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley, New York, 1997, pp. 1-100. Genetic Algorithms and Their Use in Chemistry.
2. J. Ziegler and S. Schwarzinger, J. Computer-Aided Mol. Design, 20, 47 (2006). Genetic Algorithms as a Tool for Helix Design - Computational and Experimental Studies on Prion Protein Helix 1.
3. G. A. Cox and R. L. Johnston, J. Chem. Phys., 124, 204714 (2006). Analyzing Energy Landscapes for Folding Model Proteins.
4. D. P. Djurdjevic and M. J. Biggs, J. Comput. Chem., 27, 1177 (2006). Ab initio Protein Fold Prediction Using Evolutionary Algorithms: Influence of Design and Control Parameters on Performance.
5. C. E. W. Gributs and D. H. Burns, Chemometrics and Intelligent Lab. Systems, 83, 44 (2006). Parsimonious Calibration Models for Near-Infrared Spectroscopy using Wavelets and Scaling Functions.
6. C. M. Sunderling, N. Sukumar, H. Zhang, M. J. Embrechts, and C. M. Breneman, in Reviews in Computational Chemistry, Vol. 22, K. B. Lipkowitz, T. R. Cundari and V. J. Gillet, Eds., Wiley-VCH, New York, 2006, pp. 295-329. Wavelets in Chemistry and Cheminformatics.
7. M. Dam and D. N. Saraf, Computers and Chemical Engineering, 30, 722 (2006). Design of Neural Networks using Genetic Algorithm for On-Line Property Estimation of Crude Fractionator Products.
8. K. L. Peterson, in Reviews in Computational Chemistry, Vol. 16, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 2000, pp. 53-140. Artificial Neural Networks and Their Use in Chemistry.
9. J. W. H. Wong, C. Durante, and H. M. Cartwright, Anal. Chem., 77, 5655 (2005). Application of Fast Fourier Transform Cross-Correlation for the Alignment of Large Chromatographic and Spectral Data Sets.
10. D. J. Livingstone and D. W. Salt, in Reviews in Computational Chemistry, Vol. 21, K. B. Lipkowitz, R. Larter and T. R. Cundari, Eds., Wiley-VCH, New York, 2005, pp. 287-348. Variable Selection - Spoilt for Choice?
11. E. Pranckeviciene, R. Somorjai, R. Baumgartner, and M.-G. Jeon, Artificial Intelligence in Medicine, 35, 215 (2005). Identification of Signatures in Biomedical Spectra using Domain Knowledge.
12. C. J. Montgomery, C. Yang, A. R. Parkinson, and J.-Y. Chen, Combustion and Flame, 144, 37 (2006). Selecting the Optimum Quasi-Steady-State Species for Reduced Chemical Kinetic Mechanisms using a Genetic Algorithm.
13. L. Elliott, D. B. Ingham, A. G. Kyne, N. S. Mera, M. Pourkashanian, and S. Whittaker, Computers and Chemical Engineering, 30, 889 (2006). Reaction Mechanism Reduction and Optimization for Modeling Aviation Fuel Oxidation using Standard and Hybrid Genetic Algorithms.
14. C. Lautenberger, G. Rein, and C. Fernandez-Pello, Fire Safety Journal, 41, 204 (2006). The Application of a Genetic Algorithm to Estimate Material Properties for Fire Modeling from Bench-Scale Fire Test Data.
15. K. B. McGrattan, G. P. Forney, J. E. Floyd, S. Hostikka, and K. O. Prasad, Fire Dynamics Simulator (version 4) user's guide. National Institute of Standards and Technology, NISTIR 6784, 2004.
16. A. Gondarenko, S. Preble, J. Robinson, L. Chen, H. Lipson, and M. Lipson, Phys. Rev. Lett.,96, 143904 (2006). Spontaneous Emergence of Periodic Patterns in a Biologically Inspired Simulation of Photonic Structures.
17. 2 from Synchrotron Powder Diffraction Data.
18. A. R. Oganov and C. W. Glass, J. Chem. Phys., 124, 244704 (2006). Crystal Structure Prediction using Ab Initio Evolutionary Techniques: Principles and Applications.
19. N. L. Abraham and M. I. J. Probert, Phys. Rev. B, 73, 224104 (2006). A Periodic Genetic Algorithm with Real-Space Representation for Crystal Structure and Polymorph Prediction.
20. H. M. Cartwright, unpublished work.
21. P. J. Hsu and S. K. Lai, J. Chem. Phys., 124, 044711 (2006). Structures of Bimetallic Clusters.
22. F. Cleri and V. Rosato, Phys. Rev. B., 48, 22 (1993). Tight-Binding Potentials for Transition Metals and Alloys.
23. 60
24. B. Ahlswede and K. Jug, J. Comput. Chem., 20, 563 (1999). Consistent Modifications of SINDO1. I. Approximations and Parameters.
25. L. R. Marim, M. R. Lemes, and A. Dal Pino Jr., Phys. Stat. Sol. (B) - Basic Solid State Physics, 243, 449 (2006). Investigation of Prolate and Near Spherical Geometries of Mid-Sized Silicon Clusters.
26. P. Juhas, D. M. Cherba, P. M. Duxbury, W. F. Punch, and S. J. L. Billinge, Nature, 440/30, 655 (2006). Ab Initio Determination of Solid-State Nanostructure.
27. K.-S. Sohn, D. H. Park, S. H. Cho, B. I. Kim, and S. I. Woo. J. Comb. Chem., 8, 44 (2006). Genetic Algorithm-Assisted Combinatorial Search for a new Green Phosphor for use in Tricolor White LEDs.
28. G. Grubert, S. Kolf, M. Baerns, I. Vauthey, D. Farrusseng, A. C. van Veen, C. Mirodatos, E. R. Stobbe, and P. D. Cobden, Applied Catalysis A: General, 306, 17 (2006). Discovery of New Catalytic Materials for the Water-Gas Shift Reaction by High Throughput Experimentation.
29. P. Watkins and G. Puxty, Talanta, 68, 1336 (2006). A Hybrid Genetic Algorithm for Estimating the Equilibrium Potential of an Ion-Selective Electrode.
30. E. C. Rivera, A. C. Costa, D. I. P. Atala, F. Maugeri, M. R. Wolf Maciel, and R. M. Filho, Process Biochem., 41, 1682 (2006). Evaluation of Optimization Techniques for Parameter Estimation: Application to Ethanol Fermentation Considering the Effect of Temperature.
31. See, for example, M. Arakawa, M. Hasegawa, and K. Funatsu, J. Chem. Inform. Comput. Sci., 43, 1390 (2003). Novel Alignment Method of Small Molecules Using the Hopfield Neural Network.
32. R. Sharda and D. Delen, Expert Systems with Applications, 30, 243 (2006). Predicting Box-Office Success of Motion Pictures with Neural Networks.
33. L. M. Fu, and E. H. Shortliffe, IEEE Trans. On Neural Nets, 11, 647 (2000). The Application of Certainty Factors to Neural Computing for Rule Discovery.
34. M. H. Keshavarz and M Jaafari, Propellants, Explosives, Pyrotech., 31, 216 (2006). Investigation of the Various Structure Parameters for Predicting Impact Sensitivity of Energetic Molecules via Artificial Neural Network.
35. R. C. Santana, P. T. Do, M. Santikunaporn, W. E. Alvarez, J. D. Taylor, E. L. Sughrue, and D.E. Resasco, Fuel, 85, 643 (2006). Evaluation of Different Reaction Strategies for the Improvement of Cetane Number in Diesel Fuels.
36. A. R. Katritzky, L. M. Pacureanu, D. A. Dobchev, D. C. Fara, P. R. Duchowicz, and M. Karelson, Bioorganic and Medicinal Chemistry, 14, 4987 (2006). QSAR Modeling of the Inhibition of Glycogen Synthase Kinase-3.
37. L. Molnar, G. M. Keseru, A. Papp, Z. Lorincz, G. Ambrus, and F. Darvas, Bioorganic and Medicinal Chemistry Letters, 16, 1037 (2005). A Neural Network Based Classification Scheme for Cytotoxicity Predictions: Validation on 30,000 Compounds.
38. K. S. Schmitz, An Introduction to Dynamic Light Scattering by Macromolecules. Academic Press, London, 1990.
39. V. V. Berdnik, K. Gilev, A. Shvalov, V. Maltsev, and V. A. Loiko, J. Quant. Spectrosc. & Radiative Transfer., 102, 62 (2006). Characterization of Spherical Particles using High-Order Neural Networks and Scanning Flow Cytometry.
40. A. V. Kalach, Russian Chem. Bull. Int. Edn., 55, 212 (2006). Using Artificial Neural Networks for Prediction of Organic Acid Partition Coefficients.
41. Y. Zhang, H. Li, A. Hou, and J. Havel, Chemometrics and Intell. Lab. Systems, 82, 165 (2006). Artificial Neural Networks Based on Principal Component Analysis Input Selection for Quantification in Overlapped Capillary Electrophoresis Peaks.
42. Y. Shan and A. Seidel-Morgenstern, J. Chromatogr. A, 1093, 47 (2005). Optimization of Gradient Elution Conditions in Multicomponent Preparative Liquid Chromatography.
43. L. Luo, X-Ray Spectrom., 35, 215 (2006). Chemometrics and its Applications to X-Ray Spectrometry.
44. P. Koehl, in Reviews in Computational Chemistry, Vol. 22, K. B. Lipkowitz, T. R. Cundari and V. J. Gillet, Eds., Wiley-VCH, New York, 2006, pp. 1-55. Protein Structure Classification.
45. J. Cheng, M. J. Sweredoski, and P. Baldi, Data Mining Knowledge Disc., 13, 1 (2006). DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility and Recursive Neural Networks.
46. A. Passerini, M. Punta, A. Ceroni, B. Rost, and P. Frasconi, PROTEINS: Structure, Function and Bioinform., 65, 305 (2006). Identifying Cysteines and Histidines in Transition-Metal-Binding Sites Using Support Vector Machines and Neural Networks.
47. Y. Wang, L.-Y. Wu, X.-S. Zhang, and L. Chen, TAMC LNCS, 3959, 505 (2006). Automatic Classification of Protein Structures Based on Convex Hull Representation by Integrated Neural Network.
48. O. Gualdron, E. Llobet, J. Brezmes, X. Vilanova, and X. Correig, Sensors and Actuators B, 114, 522 (2006). Coupling Fast Variable Selection Methods to Neural Network-Based Classifiers: Application to Multi-Sensor Systems.
49. C.-C. Huang and T.-T. Tang, J. Appl. Polymer Sci., 100, 2532 (2006). Optimizing Multiple Qualities in As-spun Polypropylene Yarn by Neural Networks and Genetic Algorithms.
50. H. M. Cartwright, L. Sztandera, and C.-C. Chu, NTC Ann. Rep., Sept. 2005. Genetic Algorithms in Molecular Design of Novel Fibers.
51. P. P. Palmes and S. Usui, BioSystems, 82, 168 (2005). Robustness, Evolvability and Optimality of Evolutionary Neural Networks.
52. S. E. Fahlman, and C. Lebiere in Advances in Neural Information Processing Systems, D. S. Touretzky, Ed., Morgan Kaufmann, San Mateo, California, 1990 pp. 524-532. The Cascade-Correlation Learning Architecture.
53. See, for example, R. Linker, J. Chemometrics, 19, 492 (2005). Spectrum Analysis by Recursively Pruned Extended Auto-Associative Neural Network.
54. C. G. Zampronio, J. J. R. Rohwedder, and R. J. Poppi, Chemometrics and Intell. Lab. Syst., 62, 17 (2002). Artificial Neural Networks Applied to Potentiometric Acid-Base Flow Injection Titrations.
55. X.-H. Song, J. Xu, and R.-Q. Yu, Mikrochim. Acta, 111, 199 (1993). Artificial Neural Networks Applied to Potentiometric Titration of Multi-Component Polybasic Acid Mixtures.
56. T. Kohonen, Biol. Cybern., 43, 59 (1982). Self-Organized Formation of Topologically Correct Feature Maps.
57. A. J. Walker, S. S. Cross, and R. F. Harrison, Lancet, 354, 1518 (1999). Visualisation of Biomedical Datasets by Use of Growing Cell Structure Networks: A Novel Diagnostic Classification Technique.
58. X.-H. Song and P. K. Hopke, Analytica Chemica Acta, 334, 57 (1996). Kohonen Neural Network as a Pattern Recognition Method Based on the Weight Interpretation.
59. P. Ertl, S. Jelfs, J. Muhlbacher, A. Schuffenhauer, and P. Selzer. J. Med. Chem., 49, 4568 (2006). Quest for the Rings. In Silico Exploration of Ring Universe to Identify Novel Bioactive Heteroatomic Scaffolds.
60. M. Zuppa, C. Distante, P. Siciliano, and K. C. Persaud, Sensors and Actuators B, 98, 305 (2004). Drift Counteraction with Multiple Self-Organising Maps for an Electronic Nose.
61. J. W. H. Wong and H. M. Cartwright, J. Biomed. Inform., 38, 322 (2005). Deterministic Projection of Growing Cell Structure Networks for Visualization of High-Dimensionality Datasets.
62. L. A. Dobrzanski and J. Madejski, J. Materials Processing Technol., 175, 163 (2006). Prototype of an Expert System for Selection of Coatings for Metals.
63. B. Hinkemeyer, N. Januszewski, and B. A. Julstrom, Expert Syst. with Applicat., 30, 282 (2006). An Expert System for Evaluating Siberian Huskies.