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Volumn 18, Issue , 2004, Pages 2717-2724

Particle swarm optimization and neural network application for QSAR

Author keywords

[No Author keywords available]

Indexed keywords

BINARY PARTICLE SWARM OPTIMIZATION (BPSO); FEATURE SELECTION; PROCESSING ELEMENTS (PE); STANDARD DEVIATION;

EID: 12444297957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (18)

References (16)
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    • Rogers, D.1    Hopfinger, A.J.2
  • 2
    • 0003322956 scopus 로고
    • Exploring QSAR, fundamentals and applications in chemistry and biology
    • Eds. Heller. American Chemical Society, Washington, D.C.
    • C. Hansch, A. Leo, R. Stephen, and Eds. Heller. Exploring QSAR, Fundamentals and Applications in Chemistry and Biology. ACS professional Reference Book., American Chemical Society, Washington, D.C., 1995.
    • (1995) ACS Professional Reference Book
    • Hansch, C.1    Leo, A.2    Stephen, R.3
  • 3
    • 45249127902 scopus 로고
    • Principal components analysis and partial least squares regression
    • W.D. Glen, W. J. Dunn, and R. D. Scott. Principal Components Analysis and Partial Least Squares Regression. Tetrahedron Comput. Methodol.,1989, 2, 349-376.
    • (1989) Tetrahedron Comput. Methodol. , vol.2 , pp. 349-376
    • Glen, W.D.1    Dunn, W.J.2    Scott, R.D.3
  • 4
    • 12444286760 scopus 로고
    • The use of neural-networks for variable selection in QSAR
    • J. Wikel, and E. Dow. The Use of Neural-Networks for Variable Selection in QSAR. Bioorg. Med. Chem. Soc., 1988, 110, 5959-5967.
    • (1988) Bioorg. Med. Chem. Soc. , vol.110 , pp. 5959-5967
    • Wikel, J.1    Dow, E.2
  • 5
    • 85002377847 scopus 로고
    • Genetic algorithms as a strategy for feature selection
    • R. Leardi, R. Bogga, and M. Terrille. Genetic Algorithms as a Strategy for Feature selection. J. Chemometrics, 1992, 6, 267-281.
    • (1992) J. Chemometrics , vol.6 , pp. 267-281
    • Leardi, R.1    Bogga, R.2    Terrille, M.3
  • 7
    • 0037186503 scopus 로고    scopus 로고
    • Feature selection for structure-activity correlation using binary paricle swarms
    • D. K. Agrafiotis, and W. Cedeno, Feature Selection for Structure-Activity Correlation Using Binary Paricle Swarms. J. Med. Chem., 2002, 45, 1098.
    • (2002) J. Med. Chem. , vol.45 , pp. 1098
    • Agrafiotis, D.K.1    Cedeno, W.2
  • 8
    • 0003495934 scopus 로고
    • Bagging predictors
    • Department of Statistics, University of California, Berkeley
    • L. Breiman, Bagging predictors, Technical Report No. 421, Department of Statistics, University of California, Berkeley, 1994.
    • (1994) Technical Report No. 421 , vol.421
    • Breiman, L.1
  • 10
    • 0028344873 scopus 로고
    • On identifying likely determinants of biological activity in high dimensional QSAR problem
    • J. McFarland, and D. Gans. On Identifying Likely Determinants of Biological Activity in High Dimensional QSAR Problem, Quant. Structure-Act. Relat., 1994, 13, 11.
    • (1994) Quant. Structure-act. Relat. , vol.13 , pp. 11
    • McFarland, J.1    Gans, D.2
  • 11
    • 0028597381 scopus 로고
    • Variable selection in QSAR studies. II. A highly efficient combination of systematic search and evolution
    • H. Kubinyi; Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution, Quant. Structure-Act. Relat., 1994, 13, 393.
    • (1994) Quant. Structure-act. Relat. , vol.13 , pp. 393
    • Kubinyi, H.1
  • 12
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    • Development and validation of a novel variable selection technique with application to multidimensional quantitative structure-activity relationship studies
    • C. Waller, and M. Bradley. Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure-Activity Relationship Studies, J Chem. Inf. Comput. Sci.,1999, 39, 345.
    • (1999) J Chem. Inf. Comput. Sci. , vol.39 , pp. 345
    • Waller, C.1    Bradley, M.2
  • 13
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    • Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks
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    • Maddalena, D.J.1    Johnston, G.A.R.2
  • 16
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    • Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
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    • Andrea, T.A.1    Kalayeh, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.