StructRank: A New Approach for Ligand-Based Virtual Screening
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Klaus-Robert Müller | Ulf Brefeld | Fabian Rathke | Katja Hansen | K. Müller | Ulf Brefeld | K. Hansen | Fabian Rathke
[1] Klaus-Robert Müller,et al. From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ , 2010, ChemMedChem.
[2] Klaus-Robert Müller,et al. Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach , 2007, J. Chem. Inf. Model..
[3] R. Erikson,et al. Expression of a mitogen-responsive gene encoding prostaglandin synthase is regulated by mRNA splicing. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[4] W. K. Sonnenburg,et al. Concentrations of prostaglandin endoperoxide synthase and prostaglandin I2 synthase in the endothelium and smooth muscle of bovine aorta. , 1983, The Journal of clinical investigation.
[5] Klaus Obermayer,et al. Support vector learning for ordinal regression , 1999 .
[6] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.
[7] Tomasz Arodz,et al. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. , 2006, Combinatorial chemistry & high throughput screening.
[8] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[9] Thomas Lengauer,et al. Novel technologies for virtual screening. , 2004, Drug discovery today.
[10] Mark A. Murcko,et al. Virtual screening : an overview , 1998 .
[11] Shivani Agarwal,et al. Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach , 2010, J. Chem. Inf. Model..
[12] Klaus-Robert Müller,et al. Machine learning models for lipophilicity and their domain of applicability. , 2007, Molecular pharmaceutics.
[13] Anthony Nicholls,et al. What do we know and when do we know it? , 2008, J. Comput. Aided Mol. Des..
[14] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[15] Brian K. Shoichet,et al. Virtual screening of chemical libraries , 2004, Nature.
[16] Ulf Brefeld,et al. Semi-supervised learning for structured output variables , 2006, ICML.
[17] Tim D. J. Perkins,et al. Large-scale virtual screening for discovering leads in the postgenomic era , 2001, IBM Syst. J..
[18] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[19] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[20] P. Molinoff,et al. Basic Neurochemistry: Molecular, Cellular and Medical Aspects , 1989 .
[21] Haifeng Chen,et al. Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression , 2004, J. Chem. Inf. Model..
[22] Quoc V. Le. Optimization of Ranking Measures , 2007 .
[23] Jeffrey M Drazen,et al. COX-2 inhibitors--a lesson in unexpected problems. , 2005, The New England journal of medicine.
[24] Jens Sadowski,et al. Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..
[25] P. Charifson,et al. Improved scoring of ligand-protein interactions using OWFEG free energy grids. , 2001, Journal of medicinal chemistry.
[26] Paul D Lyne,et al. Structure-based virtual screening: an overview. , 2002, Drug discovery today.
[27] J. Bertino,et al. Karnofsky memorial lecture. Ode to methotrexate. , 1993, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[28] Hengzhi Liu,et al. QSAR Study of Ethyl 2‐[(3‐Methyl‐2,5‐dioxo(3‐pyrrolinyl))amino] ‐4‐(trifluoromethyl)pyrimidine‐5‐carboxylate: An Inhibitor of AP‐1 and NF‐ϰB Mediated Gene Expression Based on Support Vector Machines. , 2003 .
[29] Roberto Todeschini,et al. Handbook of Molecular Descriptors , 2002 .
[30] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[31] Thorsten Joachims,et al. Support Vector Training of Protein Alignment Models , 2007, RECOMB.
[32] Jeffrey J. Sutherland,et al. Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure-Activity Relationships , 2003, J. Chem. Inf. Comput. Sci..
[33] Jonathan D Hirst,et al. Machine learning in virtual screening. , 2009, Combinatorial chemistry & high throughput screening.
[34] Tao Qin,et al. LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval , 2007 .
[35] Anne Mai Wassermann,et al. Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors , 2009, J. Chem. Inf. Model..
[36] Jürgen Bajorath,et al. New methodologies for ligand-based virtual screening. , 2005, Current pharmaceutical design.
[37] Olivier Chapelle,et al. Training a Support Vector Machine in the Primal , 2007, Neural Computation.
[38] Johann Gasteiger,et al. Multivariate structure‐activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method , 1984 .
[39] Yuan-Ping Pang,et al. Successful virtual screening of a chemical database for farnesyltransferase inhibitor leads. , 2000, Journal of medicinal chemistry.
[40] Alexander J. Smola,et al. Support Vector Regression Machines , 1996, NIPS.
[41] Jan M. Kriegl,et al. Prediction of Human Cytochrome P450 Inhibition Using Support Vector Machines , 2005 .
[42] Marvin Johnson,et al. Concepts and applications of molecular similarity , 1990 .
[43] Jaana Kekäläinen,et al. IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.
[44] Christopher I. Bayly,et al. Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..
[45] Robert P. Sheridan,et al. Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches , 2001, J. Chem. Inf. Comput. Sci..
[46] Filip Radlinski,et al. A support vector method for optimizing average precision , 2007, SIGIR.
[47] O. Chapelle. Large margin optimization of ranking measures , 2007 .
[48] E. Fluder,et al. Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches. , 2001 .