Моделирование свойств химических соединений с использованием искусственных нейронных сетей и фрагментных дескрипторов (1097754), страница 60
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Sci. - 2003. V. 43, № 1. - P. 75-84.270. Gonzalez M.P.; Helguera A.M.; Diaz H.G. A TOPS-MODE approach to predict permeability coefficients. // Polymer. - 2004. - V. 45, № 6. - P. 2073-2079.271. Estrada E.; Molina E.; Perdomo-Lopez I. Can 3D Structural Parameters BePredicted from 2D (Topological) Molecular Descriptors? // J.
Chem. Inf. Comput.Sci. - 2001. - V. 41, № 4. - P. 1015-1021.272. Estrada E.; Uriarte E.; Montero A.; Teijeira M.; Santana L.; De Clercq E. Anovel approach for the virtual screening and rational design of anticancer compounds.// J Med Chem. - 2000. - V. 43, № 10. - P. 1975-1985.273. Estrada E.; Vilar S.; Uriarte E.; Gutierrez Y. In Silico Studies toward the Discovery of New Anti-HIV Nucleoside Compounds with the Use of TOPS-MODE and2D/3D Connectivity Indices. 1. Pyrimidyl Derivatives. // J.
Chem. Inf. Comput. Sci.- 2002. - V. 42, № 5. - P. 1194-1203.274. Estrada E.; Patlewicz G.; Gutierrez Y. From Knowledge Generation to Knowledge Archive. A General Strategy Using TOPS-MODE with DEREK To FormulateNew Alerts for Skin Sensitization. // J. Chem. Inf. Comput.
Sci. - 2004. - V. 44, № 2.- P. 688-698.275. Gonzalez M.P.; Diaz H.G.; Ruiz R.M.; Cabrera M.A.; de Armas R.R. TOPSMODE based QSARs derived from heterogeneous series of compounds. Applicationsto the design of new herbicides. // J. Chem. Inf. Comput. Sci. - 2003. - V. 43, № 4. P. 1192-1199.276. GonzalezM.P.;MoldesM.D.T.QSARstudyofN-6-(substituted-phenylcarbamoyl) adenosine-5 '-uronamides as agonist for A(1) adenosine receptors.// Bull.
Math. Biol. - 2004. - V. 66, № 4. - P. 907-920.277. Gonzalez M.P.; Dias L.C.; Helguera A.M.; Rodriguez Y.M.; de Oliveira L.G.;Gomez L.T.; Diaz H.G. TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds.// Bioorganic & Medicinal Chemistry. - 2004. - V. 12, № 16. - P. 4467-4475.339278. Molina E.; Gonzales Diaz H.; Gonzalez M.P.; Rodriguez E.; Uriarte E.
Designing Antibacterial Compounds through a Topological Substructural Approach. // J.Chem. Inf. Comput. Sci. - 2004. - V. 44, № 2. - P. 515-521.279. Gonzalez M.P.; Diaz H.G.; Cabrera M.A.; Ruiz R.M. A novel approach to predict a toxicological property of aromatic compounds in the Tetrahymena pyriformis.// Bioorg. Med, Chem. - 2004. - V. 12, № 4. - P. 735-744.280. Helguera A.M.; Gonzalez M.P.; Briones J.R. TOPS-MODE approach to predict mutagenicity in dental monomers. // Polymer.
- 2004. - V. 45, № 6. - P. 20452050.281. Gonzalez M.P.; Dias L.C.; Helguera A.M. A topological sub-structural approach to the mutagenic activity in dental monomers. 2. Cycloaliphatic epoxides. //Polymer. - 2004. - V. 45, № 15. - P. 5353-5359.282. Gonzalez M.P.; Moldes M.d.C.T.; Fall Y.; Dias L.C.; Helguera A.M. A topological sub-structural approach to the mutagenic activity in dental monomers. 3.
Heterogeneous set of compounds. // Polymer. - 2005. - V. 46, № 8. - P. 2783-2790.283. Kramer S.; De Raedt L.; Helma C. In Molecular feature mining in HIV data,Seventh ACM SIGKDD international conference on Knowledge discovery and datamining, San Francisco, California, August 26 - 29, 2001, 2001; ACM Press, NewYork, NY: San Francisco, California. - 2001. - P. 136-143.284.
De Raedt L.; Kramer S. In The Levelwise Version Space Algorithm and itsApplication to Molecular Fragment Finding, The Seventeenth International JointConference on Articial Intelligence, 2001; Morgan Kaufmann: San Francisco. - 2001.- P. 853-862.285. Kramer S.; De Raedt L. In Feature construction with version spaces for biochemical applications, The eighteenth International Conference on Machine Learning, 2001; Morgan Kaufmann: San Francisco, CA. - 2001. - P. 258-265.286. Inokuchi A. Mining Generalized Substructures from a Set of Labeled Graphs.
//Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04)- IEEE Computer Society. - 2004. - P. 415-418287. Yan X.; Han J. gspan: Graph-based substructure pattern mining. // Proceedingsof the 2002 IEEE International Conference on Data Mining. - 2002. - P. 721-724.340288. Saigo H.; Kadowaki T.; Tsuda K. In A Linear Programming Approach for Molecular QSAR analysis, International Workshop on Mining and Learning with Graphs2006. - 2006. - P.
85-96.289. Asai T.; Abe K.; Kawasoe S.; Arimura H.; Satamoto H.; Arikawa S. EfficientSubstructure Discovery from Large Semi-structured Data. // SIAM SDM'02. - 2002.290. Chi Y.; Muntz R.R.; Nijssen S.; Kok J.N. Frequent subtree mining -- an overview. // Fundamenta Informaticae - 2005. - V. 66, № 1-2. - P. 161-198.291.
Inokuchi A.; Washio T.; Motoda H. In An Apriori-Based Algorithm for MiningFrequent Substructures from Graph Data, 4th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD), Lyon, France, September 2000,2000; Lyon, France, September 2000. - 2000. - P. 13-23.292. Kuramochi M.; Karypis G. In Frequent Subgraph Discovery, 1st IEEE Conference on Data Mining, 2001.
- 2001. - P. 313-320.293. Borgelt C.; Meinl T.; Berthold M. MoSS: A Program for Molecular Substructure Mining. // Proceedings of the 1st international Workshop on Open Source DataMining: Frequent Pattern Mining Implementations ACM Press, New York, NY: Chicago, Illinois, August 21 - 21, 2005. - 2005. - P. 6-15.294. Zaki M.J. Efficiently mining frequent trees in a forest. // Proceedings of theeighth ACM SIGKDD international conference on Knowledge discovery and datamining, ACM Press: Edmonton, Alberta, Canada.
- 2002. - P. 71-80295. Chi Y.; Yang Y.; Muntz R.R. HybridTreeMiner: An efficient algorithm for mining frequent rooted trees and free trees using canonical forms. // The 16th International Conference on Scientific and Statistical Database Management (SSDBM'04),June 2004. - 2004.296. Chi Y.; Yang Y.; Xia Y.; Muntz R.R. CMTreeMiner: Mining both closed andmaximal frequent subtrees. // The Eighth Pacific Asia Conference on KnowledgeDiscovery and Data Mining (PAKDD'04), May 2004. - 2004.297.
Dehaspe L.; Toivonen H.; King R.D. Finding frequent substructures in chemical compounds. // 4th International Conference on Knowledge Discovery and DataMining, Agrawal R.; Stolorz P.; Piatetsky-Shapiro G., Eds. AAAI Press. - 1998. - P.30-36.341298. Deshpande M.; Kuramochi M.; Karypis G. Frequent sub-structure based approaches for classifying chemical compounds. // Proceedings of the Third IEEE international Conference on Data Mining (November 19 - 22, 2003).
ICDM., IEEEComputer Society, Washington, DC. - 2003. - P. 35-49.299. Demiriz A.; Bennett K.P.; Shawe-Taylor J. Linear Programming Boosting viaColumn Generation. // Mach. Learn. - 2002. - V. 46, № 1-3. - P. 225-254.300. Graham D.J.; Malarkey C.; Schulmerich M.V. Information Content in OrganicMolecules: Quantification and Statistical Structure via Brownian Processing. // J.Chem.
Inf. Comput. Sci. - 2004. - V. 44, № 5. - P. 1601-1611.301. Batista J.; Godden J.W.; Bajorath J. Assessment of molecular similarity fromthe analysis of randomly generated structural fragment populations. // J. Chem. Inf.Model. - 2006. - V. 46, № 5. - P. 1937-1944.302. Batista J.; Bajorath J. Chemical database mining through entropy-based molecular similarity assessment of randomly generated structural fragment populations.// J. Chem. Inf.
Model. - 2007. - V. 47, № 1. - P. 59-68.303. Sanderson D.M.; Earnshaw C.G. Computer prediction of possible toxic actionfrom chemical structure; the DEREK system. // Hum. Exp. Toxicol. - 1991. - V. 10,№ 4. - P. 261-273.304. Takeuchi K.; Kuroda C.; Ishida M. Prolog-based functional group perceptionand calculation of 1-octanol/water partition coefficients using Rekker's fragmentmethod. // J. Chem. Inf. Model. - 1990.
- V. 30, № 1. - P. 22-26.305. Chen L. Reaction Classification and Knowledge Acquisition. // Handbook ofChemoinformatics, Gasteiger J., Ed. Wiley-VCH: Weinheim. - 2003. - V. 1. - P. 348388.306. Dugundji J.; Ugi I. An Algebraic Model of Constitutional Chemistry as a Basisfor Chemical Computer Programs. // Topics Curr.
Chem. - 1973. - V. 39 - P. 19-64.307. Zefirov N.S.; Trach S.S. Systematization of tautomeric processes and formallogical approach to the search for new topological and reaction types of tautomerism.// Chemica Scripta. - 1980. - V. 15, № 1. - P. 4-12.308. Zefirov N.S. An approach to systematization and design of organic reactions. //Accounts of Chemical Research. - 1987. - V. 20, № 7.
- P. 237-243.342309. Vladutz G. Modern Approaches to Chemical Reaction Searching. // Approaches to Chemical Reaction Searching, Willett P., Ed. Gower: London. - 1986. P. 202-220.310. Fujita S. Description of Organic Reactions Based on Imaginary TransitionStructures. 1. Introduction of New Concepts. // J.
Chem. Inf. Comput. Sci. - 1986. V. 26, № 4. - P. 205-212.311. Fujita S. 'Structure-Reaction Type' Paradigm in the Conventional Methods ofDescribing Organic Reactions and the Concept of Imaginary Transition StructuresOvercoming This Paradigm. // J. Chem. Inf. Comput. Sci. - 1987. - V. 27, № 3. - P.120-126.312. Borodina Y.; Rudik A.; Filimonov D.; Kharchevnikova N.; Dmitriev A.; Blinova V.; Poroikov V. A New Statistical Approach to Predicting Aromatic Hydroxylation Sites. Comparison with Model-Based Approaches.