www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets
暂无分享,去创建一个
[1] H. Kubinyi. QSAR and 3D QSAR in drug design Part 1: methodology , 1997 .
[2] Tudor I. Oprea,et al. Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors , 1996, J. Comput. Aided Mol. Des..
[3] Paolo Tosco,et al. Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields , 2011, Journal of molecular modeling.
[4] Rino Ragno,et al. 3-D QSAutogrid/R: An Alternative Procedure To Build 3-D QSAR Models. Methodologies and Applications , 2012, J. Chem. Inf. Model..
[5] G. Klebe,et al. A 3D QSAR Study on a Set of Dopamine D4 Receptor Antagonists. , 2003 .
[6] P. Bharatam,et al. 3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists , 2007, Journal of molecular modeling.
[7] A. Golbraikh,et al. Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models. , 2000, European journal of medicinal chemistry.
[8] Matthew Clark,et al. The Probability of Chance Correlation Using Partial Least Squares (PLS) , 1993 .
[9] Garland R. Marshall,et al. 3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries , 1993 .
[10] Yvonne C. Martin,et al. 3D QSAR: current state, scope, and limitations , 1998 .
[11] Janet E. Jones. On the determination of molecular fields. —II. From the equation of state of a gas , 1924 .
[12] J. Dearden. The History and Development of Quantitative Structure-Activity Relationships (QSARs) , 2016 .
[13] Dennis H. Smith,et al. Applications of artificial intelligence for chemical inference. 37. GENOA: a computer program for structure elucidation utilizing overlapping and alternative substructures , 1981 .
[14] Gerd Folkers,et al. 3D QSAR in drug design. Vol. 2, Ligand-protein interactions andmolecular similarity , 1998 .
[15] Renxiao Wang,et al. All-Orientation Search and All-Placement Search in Comparative Molecular Field Analysis , 1998 .
[16] G. Cruciani,et al. Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. , 1994, Journal of medicinal chemistry.
[17] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[18] Jitender Verma,et al. 3D-QSAR in drug design--a review. , 2010, Current topics in medicinal chemistry.
[19] J. Sutherland,et al. A comparison of methods for modeling quantitative structure-activity relationships. , 2004, Journal of medicinal chemistry.
[20] Kenneth M. Merz,et al. Drug Design : Structure-and Ligand-Based Approaches , 2017 .
[21] Jaroslaw Polanski,et al. GRID Formalism for the Comparative Molecular Surface Analysis: Application to the CoMFA Benchmark Steroids, Azo Dyes, and HEPT Derivatives. , 2004 .
[22] J. Topliss,et al. Change correlations in structure-activity studies using multiple regression analysis. , 1972, Journal of Medicinal Chemistry.
[23] R. Shoemaker,et al. A Three-Dimensional Quantitative Structure-Activity Relationship Study of the Inhibition of the ATPase Activity and the Strand Passing Catalytic Activity of Topoisomerase IIα by Substituted Purine Analogs , 2006, Molecular Pharmacology.
[24] R. Cramer,et al. Recent advances in comparative molecular field analysis (CoMFA). , 1989, Progress in clinical and biological research.
[25] S. Free,et al. A MATHEMATICAL CONTRIBUTION TO STRUCTURE-ACTIVITY STUDIES. , 1964, Journal of medicinal chemistry.
[26] Seung Joo Cho,et al. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) Study of Mutagen X , 2004 .
[27] G. Klebe,et al. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.
[28] Na Zhang,et al. 3D QSAR for GSK-3beta inhibition by indirubin analogues. , 2006, European journal of medicinal chemistry.
[29] A. Duranti,et al. Application of 3D‐QSAR in the Rational Design of Receptor Ligands and Enzyme Inhibitors , 2005, Chemistry & biodiversity.
[30] Jonathan D. Hirst,et al. On the Stability of CoMFA Models , 2004, J. Chem. Inf. Model..
[31] H. Hotelling. The relations of the newer multivariate statistical methods to factor analysis. , 1957 .
[32] Lisa Harris,et al. Partial Charge Calculation Method Affects CoMFA QSAR Prediction Accuracy , 2009, J. Chem. Inf. Model..
[33] Rahul Jain,et al. 3D-QSAR study of ring-substituted quinoline class of anti-tuberculosis agents. , 2006, Bioorganic & medicinal chemistry.
[34] Gordon M. Crippen,et al. Validation of DAPPER for 3D QSAR: Conformational Search and Chirality Metric , 2003, J. Chem. Inf. Comput. Sci..
[35] N E Jewell,et al. Automatic generation of alignments for 3D QSAR analyses. , 2001, Journal of molecular graphics & modelling.
[36] Eugene A. Coats,et al. The CoMFA Steroids as a Benchmark Dataset for Development of 3D QSAR Methods , 1998 .
[37] Angel Herráez,et al. Biomolecules in the computer: Jmol to the rescue , 2006, Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology.
[38] Silvio Massa,et al. 3-D QSAR Studies on Histone Deacetylase Inhibitors. A GOLPE/GRID Approach on Different Series of Compounds , 2006, J. Chem. Inf. Model..
[39] Miki Akamatsu,et al. Current state and perspectives of 3D-QSAR. , 2002, Current topics in medicinal chemistry.
[40] P. Skolnick,et al. Synthetic and computer-assisted analysis of the structural requirements for selective, high-affinity ligand binding to diazepam-insensitive benzodiazepine receptors. , 1993, Journal of medicinal chemistry.
[41] J. Sussman,et al. JSmol and the Next-Generation Web-Based Representation of 3D Molecular Structure as Applied to Proteopedia , 2013 .
[42] H. Kubinyi. QSAR and 3D QSAR in drug design Part 2: applications and problems , 1997 .
[43] P. Greengard,et al. Structural basis for the synthesis of indirubins as potent and selective inhibitors of glycogen synthase kinase-3 and cyclin-dependent kinases. , 2004, Journal of medicinal chemistry.
[44] D. Maddalena,et al. Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks. , 1995, Journal of medicinal chemistry.
[45] G Klebe,et al. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. , 1999, Journal of medicinal chemistry.
[46] Yun Xu,et al. On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning , 2018, Journal of Analysis and Testing.
[47] J. Topliss,et al. CHANCE FACTORS IN STUDIES OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS , 1980 .
[48] Gisbert Schneider,et al. Active-learning strategies in computer-assisted drug discovery. , 2015, Drug discovery today.
[49] Chaohong Sun,et al. Discovery of N-(4-(2,4-Difluorophenoxy)-3-(6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridin-4-yl)phenyl)ethanesulfonamide (ABBV-075/Mivebresib), a Potent and Orally Available Bromodomain and Extraterminal Domain (BET) Family Bromodomain Inhibitor. , 2017, Journal of Medicinal Chemistry.
[50] Yong-Jun Jiang,et al. 3D QSAR for GSK-3β inhibition by indirubin analogues , 2006 .
[51] Jeffrey J. Sutherland,et al. Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors , 2004, J. Comput. Aided Mol. Des..
[52] D. Lesieur,et al. Three-dimensional quantitative structure-activity relationships of cyclo-oxygenase-2 (COX-2) inhibitors: a comparative molecular field analysis. , 2001, Journal of medicinal chemistry.
[53] Jacob Cohen,et al. Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .
[54] Vasant Ranade. Molecular Interaction Fields , 2006 .
[55] Artem Cherkasov,et al. An updated steroid benchmark set and its application in the discovery of novel nanomolar ligands of sex hormone-binding globulin. , 2008, Journal of medicinal chemistry.
[56] Hongbin Yuan,et al. CoMFA study of piperidine analogues of cocaine at the dopamine transporter: exploring the binding mode of the 3 alpha-substituent of the piperidine ring using pharmacophore-based flexible alignment. , 2004, Journal of medicinal chemistry.
[57] R. Cramer,et al. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.
[58] P. Goodford. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.
[59] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[60] Jeffrey M. Perkel,et al. Programming: Pick up Python , 2015, Nature.
[61] Tommi H. Nyrönen,et al. Comparing the Quality and Predictiveness between 3D QSAR Models Obtained from Manual and Automated Alignment , 2004, J. Chem. Inf. Model..
[62] H. Kubinyi,et al. 3D QSAR in drug design. , 2002 .
[63] Stefano Alcaro,et al. Molecular interaction fields in drug discovery: recent advances and future perspectives , 2013 .
[64] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[65] G. Klebe,et al. DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. , 2002, Journal of medicinal chemistry.
[66] W. Delano. The PyMOL Molecular Graphics System , 2002 .
[67] Antti Poso,et al. 3D-QSAR studies on cannabinoid CB1 receptor agonists: G-protein activation as biological data. , 2006, Journal of medicinal chemistry.
[68] Gabriele Cruciani,et al. Molecular interaction fields : applications in drug discovery and ADME prediction , 2005 .
[69] Tudor I. Oprea,et al. A Different Method for Steric Field Evaluation in CoMFA Improves Model Robustness , 1997, J. Chem. Inf. Comput. Sci..
[70] J. Topliss,et al. Chance correlations in structure-activity studies using multiple regression analysis , 1972 .
[71] Gerta Rücker,et al. y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..
[72] Egon L. Willighagen,et al. Chemical Markup, XML, and the World Wide Web. 5. Applications of Chemical Metadata in RSS Aggregators , 2004, J. Chem. Inf. Model..
[73] Xin Hu,et al. Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors. , 2005, Bioorganic & medicinal chemistry.
[74] S. Wold,et al. PLS: Partial Least Squares Projections to Latent Structures , 1993 .
[75] Yun Tang,et al. Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. , 2006, Bioorganic & medicinal chemistry.
[76] Alexander Tropsha,et al. Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.
[77] J. Dearden,et al. QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.
[78] K. Hakkarainen,et al. The Knowledge Creation Metaphor – An Emergent Epistemological Approach to Learning , 2005 .
[79] David T. Stanton,et al. On the Physical Interpretation of QSAR Models , 2003, J. Chem. Inf. Comput. Sci..
[80] C. Hansch,et al. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .
[81] Carlo Scolastico,et al. A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists , 1996, J. Comput. Aided Mol. Des..
[82] Glen E Kellogg,et al. 3D QSAR in modern drug design. , 2003, EXS.