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Volumn 2, Issue 9, 2007, Pages 1265-1267

Predicting lipophilicity of drug-discovery molecules using Gaussian process models

Author keywords

Domain of applicability; Drug design; Gaussian process; Lipophilicity; Machine learning

Indexed keywords

ARTICLE; DRUG DESIGN; THEORETICAL MODEL;

EID: 44449148610     PISSN: 18607179     EISSN: 18607187     Source Type: Journal    
DOI: 10.1002/cmdc.200700041     Document Type: Article
Times cited : (32)

References (15)
  • 5
    • 0004135065 scopus 로고    scopus 로고
    • Neural Networks: Tricks of the Trade
    • Springer, Berlin
    • a) G. Orr, K.-R. Müller, Neural Networks: Tricks of the Trade, LNCS, Springer, Berlin, 1998;
    • (1998) LNCS
    • Orr, G.1    Müller, K.-R.2
  • 12
    • 54549108315 scopus 로고    scopus 로고
    • Molecular Networks GmbH Computerchemie, Erlangen Germany
    • J. Sadowski, C. H. Schwab, J. Gasteiger, Corina v.3.1, Molecular Networks GmbH Computerchemie, Erlangen (Germany), 2005.
    • (2005) Corina v.3.1
    • Sadowski, J.1    Schwab, C.H.2    Gasteiger, J.3
  • 14
    • 54549115424 scopus 로고    scopus 로고
    • To speed up model training and decrease the memory demand, we employed a wrapper script to perform a k-means clustering based on descriptors and to train one GP model for each cluster of up to 5000 compounds. When applying this model, the wrapper considers each GP and chooses the prediction with the highest confidence (smallest predicted error bar).
    • To speed up model training and decrease the memory demand, we employed a wrapper script to perform a k-means clustering based on descriptors and to train one GP model for each cluster of up to 5000 compounds. When applying this model, the wrapper considers each GP and chooses the prediction with the highest confidence (smallest predicted error bar).


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.