ISIDA Property‐Labelled Fragment Descriptors
暂无分享,去创建一个
[1] R. Venkataraghavan,et al. Atom pairs as molecular features in structure-activity studies: definition and applications , 1985, J. Chem. Inf. Comput. Sci..
[2] Marvin Johnson,et al. Concepts and applications of molecular similarity , 1990 .
[3] Gerta Rücker,et al. Counts of all walks as atomic and molecular descriptors , 1993, J. Chem. Inf. Comput. Sci..
[4] Marina Lasagni,et al. New molecular descriptors for 2D and 3D structures. Theory , 1994 .
[5] Thierry Convard,et al. SmilogP: A Program for a Fast Evaluation of Theoretical Log P from the Smiles Code of a Molecule , 1994 .
[6] Schmid,et al. "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.
[7] Alexandre Varnek,et al. Modeling of Ion Complexation and Extraction Using Substructural Molecular Fragments , 2000, J. Chem. Inf. Comput. Sci..
[8] A. Good,et al. 3-D pharmacophores in drug discovery. , 2001, Current pharmaceutical design.
[9] Igor V. Tetko,et al. Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility of Chemical Compounds , 2001, J. Chem. Inf. Comput. Sci..
[10] James G. Nourse,et al. Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..
[11] Roberto Todeschini,et al. Handbook of Molecular Descriptors , 2002 .
[12] D. Horvath,et al. Neighborhood behavior. Fuzzy molecular descriptors and their influence on the relationship between structural similarity and property similarity , 2003 .
[13] Dragos Horvath,et al. Neighborhood Behavior of in Silico Structural Spaces with Respect to in Vitro Activity Spaces-A Novel Understanding of the Molecular Similarity Principle in the Context of Multiple Receptor Binding Profiles , 2003, J. Chem. Inf. Comput. Sci..
[14] Dragos Horvath,et al. Neighborhood Behavior of in Silico Structural Spaces with Respect to In Vitro Activity Spaces-A Benchmark for Neighborhood Behavior Assessment of Different in Silico Similarity Metrics , 2003, J. Chem. Inf. Comput. Sci..
[15] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[16] A. Varnek,et al. Structure—property modeling of metal binders using molecular fragments , 2004 .
[17] A. Brown,et al. Drugs, hERG and sudden death. , 2004, Cell calcium.
[18] Alexandre Varnek,et al. Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures , 2005, J. Comput. Aided Mol. Des..
[19] H. Mewes,et al. Can we estimate the accuracy of ADME-Tox predictions? , 2006, Drug discovery today.
[20] Gerald M. Maggiora,et al. On Outliers and Activity Cliffs-Why QSAR Often Disappoints , 2006, J. Chem. Inf. Model..
[21] Benjamin Parent,et al. Fuzzy Tricentric Pharmacophore Fingerprints, 1. Topological Fuzzy Pharmacophore Triplets and Adapted Molecular Similarity Scoring Schemes , 2006, J. Chem. Inf. Model..
[22] Jean-Philippe Vert,et al. Consistency and Convergence Rates of One-Class SVMs and Related Algorithms , 2006, J. Mach. Learn. Res..
[23] M. Sanguinetti,et al. hERG potassium channels and cardiac arrhythmia , 2006, Nature.
[24] Gisbert Schneider,et al. Scaffold‐Hopping: How Far Can You Jump? , 2006 .
[25] Andreas Bender,et al. Flexible 3D pharmacophores as descriptors of dynamic biological space. , 2007, Journal of molecular graphics & modelling.
[26] Olivier Sperandio,et al. MED-SuMoLig: A New Ligand-Based Screening Tool for Efficient Scaffold Hopping , 2007, J. Chem. Inf. Model..
[27] Petra Schneider,et al. Scaffold Hopping by “Fuzzy” Pharmacophores and its Application to RNA Targets , 2007, Chembiochem : a European journal of chemical biology.
[28] Jürgen Bajorath,et al. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. , 2007, Drug discovery today.
[29] D. Fourches,et al. Successful “In Silico” Design of New Efficient Uranyl Binders , 2007 .
[30] B Montgomery Pettitt,et al. The dewetting transition and the hydrophobic effect. , 2007, Journal of the American Chemical Society.
[31] Alexandre Varnek,et al. Stochastic versus Stepwise Strategies for Quantitative Structure-Activity Relationship GenerationHow Much Effort May the Mining for Successful QSAR Models Take? , 2007, J. Chem. Inf. Model..
[32] I. Tetko,et al. ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors , 2008 .
[33] Igor V. Tetko,et al. Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection , 2008, J. Chem. Inf. Model..
[34] Olivier Sperandio,et al. Combining Ligand- and Structure-Based Methods in Drug Design Projects , 2008 .
[35] Rajarshi Guha,et al. Structure-Activity Landscape Index: Identifying and Quantifying Activity Cliffs , 2008, J. Chem. Inf. Model..
[36] Stephen Muggleton,et al. Scaffold Hopping in Drug Discovery Using Inductive Logic Programming , 2008, J. Chem. Inf. Model..
[37] Eugen Lounkine,et al. Random molecular fragment methods in computational medicinal chemistry. , 2008, Current medicinal chemistry.
[38] Artem Cherkasov,et al. Using Molecular Docking, 3D-QSAR, and Cluster Analysis for Screening Structurally Diverse Data Sets of Pharmacological Interest , 2008, J. Chem. Inf. Model..
[39] Dragos Horvath,et al. Fuzzy Tricentric Pharmacophore Fingerprints. 2. Application of Topological Fuzzy Pharmacophore Triplets in Quantitative Structure-Activity Relationships , 2008, J. Chem. Inf. Model..
[40] Arthur M. Doweyko,et al. QSAR: dead or alive? , 2008, J. Comput. Aided Mol. Des..
[41] Tudor I. Oprea,et al. hERG classification model based on a combination of support vector machine method and GRIND descriptors. , 2008, Molecular pharmaceutics.
[42] R. Mannhold,et al. Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds , 2009, Journal of pharmaceutical sciences.
[43] Visakan Kadirkamanathan,et al. Analysis of Neighborhood Behavior in Lead Optimization and Array Design , 2009, J. Chem. Inf. Model..
[44] Jia Jia,et al. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. , 2009, Combinatorial chemistry & high throughput screening.
[45] J. Frearson,et al. HTS and hit finding in academia – from chemical genomics to drug discovery , 2009, Drug discovery today.
[46] Andreas Bender,et al. Prospective Validation of a Comprehensive In silico hERG Model and its Applications to Commercial Compound and Drug Databases , 2010, ChemMedChem.
[47] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[48] Gilles Marcou,et al. Local neighborhood behavior in a combinatorial library context , 2011, J. Comput. Aided Mol. Des..