分子挖掘 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "分子挖掘" in Chinese language version.

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doi.org

  • H. Fröhlich, J. K. Wegner, A. Zell, Kernel Functions for Attributed Molecular Graphs - A New Similarity Based Approach To ADME Prediction in Classification and Regression, QSAR Comb. Sci., 2006, 25, 317-326. doi:10.1002/qsar.200510135
  • P. Mahe, L. Ralaivola, V. Stoven, J. Vert, The pharmacophore kernel for virtual screening with support vector machines, J Chem Inf Model, 2006, 46, 2003-2014. doi:10.1021/ci060138m
  • L. Ralaivola, S. J. Swamidass, S. Hiroto and P. Baldi. Graph kernels for chemical informatics. Neural Networks. 2005, 18: 1093–1110 [2017-07-02]. doi:10.1016/j.neunet.2005.07.009. (原始内容存档于2015-09-24). 
  • P. Mahé and J.-P. Vert. Graph kernels based on tree patterns for molecules. Machine Learning. 2009, 75 (1): 3–35. ISSN 0885-6125. doi:10.1007/s10994-008-5086-2. 
  • J. K. Wegner, H. Fröhlich, H. Mielenz, A. Zell, Data and Graph Mining in Chemical Space for ADME and Activity Data Sets, QSAR Comb. Sci., 2006, 25, 205-220. doi:10.1002/qsar.200510009
  • S. A. Rahman, M. Bashton, G. L. Holliday, R. Schrader and J. M. Thornton, Small Molecule Subgraph Detector (SMSD) toolkit, Journal of Cheminformatics 2009, 1:12. doi:10.1186/1758-2946-1-12
  • R. D. King, A. Srinivasan, L. Dehaspe, Wamr: a data mining tool for chemical data, J. Comput.-Aid. Mol. Des., 2001, 15, 173-181. doi:10.1023/A:1008171016861
  • C. Helma, T. Cramer, S. Kramer, L. de Raedt, Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds, J. Chem. Inf. Comput. Sci., 2004, 44, 1402-1411. doi:10.1021/ci034254q
  • A. Karwath, L. D. Raedt, SMIREP: predicting chemical activity from SMILES, J Chem Inf Model, 2006, 46, 2432-2444. doi:10.1021/ci060159g
  • H. Ando, L. Dehaspe, W. Luyten, E. Craenenbroeck, H. Vandecasteele, L. Meervelt, Discovering H-Bonding Rules in Crystals with Inductive Logic Programming, Mol Pharm, 2006, 3, 665-674 . doi:10.1021/mp060034z
  • P. Mazzatorta, L. Tran, B. Schilter, M. Grigorov, Integration of Structure-Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity, J. Chem. Inf. Model., 2006, ASAP alert. doi:10.1021/ci600411v
  • I. I. Baskin, V. A. Palyulin, N. S. Zefirov. A Neural Device for Searching Direct Correlations between Structures and Properties of Organic Compounds. J. Chem. Inf. Comput. Sci. 1997, 37 (4): 715–721. doi:10.1021/ci940128y. 
  • D. B. Kireev. ChemNet: A Novel Neural Network Based Method for Graph/Property Mapping. J. Chem. Inf. Comput. Sci. 1995, 35 (2): 175–180. doi:10.1021/ci00024a001. 
  • A. M. Bianucci; Micheli, Alessio; Sperduti, Alessandro; Starita, Antonina. Application of Cascade Correlation Networks for Structures to Chemistry. Applied Intelligence. 2000, 12 (1-2): 117–146. doi:10.1023/A:1008368105614. 
  • A. Micheli, A. Sperduti, A. Starita, A. M. Bianucci. Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines. J. Chem. Inf. Comput. Sci. 2001, 41 (1): 202–218. PMID 11206375. doi:10.1021/ci9903399. 
  • A. Goulon, T. Picot, A. Duprat, G. Dreyfus. Predicting activities without computing descriptors: Graph machines for QSAR. SAR and QSAR in Environmental Research. 2007, 18 (1-2): 141–153. PMID 17365965. doi:10.1080/10629360601054313. 

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ncbi.nlm.nih.gov

  • A. Micheli, A. Sperduti, A. Starita, A. M. Bianucci. Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines. J. Chem. Inf. Comput. Sci. 2001, 41 (1): 202–218. PMID 11206375. doi:10.1021/ci9903399. 
  • A. Goulon, T. Picot, A. Duprat, G. Dreyfus. Predicting activities without computing descriptors: Graph machines for QSAR. SAR and QSAR in Environmental Research. 2007, 18 (1-2): 141–153. PMID 17365965. doi:10.1080/10629360601054313. 

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