Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification
暂无分享,去创建一个
[1] Nilanjan Adhikari,et al. Comparative validated molecular modeling of p53-HDM2 inhibitors as antiproliferative agents. , 2015, European journal of medicinal chemistry.
[2] Arun K. Ghosh,et al. Recent Progress in the Development of HIV-1 Protease Inhibitors for the Treatment of HIV/AIDS. , 2016, Journal of medicinal chemistry.
[3] Arun K. Ghosh,et al. Potent HIV Protease Inhibitors: The Development of Tetrahydrofuranylglycines as Novel P2‐Ligands and Pyrazine. Amides as P3‐Ligands. , 1993 .
[4] Jiunn H. Lin,et al. Orally bioavailable highly potent HIV protease inhibitors against PI-resistant virus. , 2005, Bioorganic & medicinal chemistry letters.
[5] D Weininger,et al. SMILES: a line notation and computerized interpreter for chemical structures. , 1987 .
[6] Design, synthesis, and biological evaluation of HIV/FIV protease inhibitors incorporating a conformationally constrained macrocycle with a small P3' residue. , 2001, Bioorganic & medicinal chemistry letters.
[7] Kunal Roy,et al. Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection , 2011 .
[8] P. Darke,et al. A series of potent HIV-1 protease inhibitors containing a hydroxyethyl secondary amine transition state isostere: synthesis, enzyme inhibition, and antiviral activity. , 1992, Journal of medicinal chemistry.
[9] Igor V. Tetko,et al. Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis , 2008, J. Chem. Inf. Model..
[10] W. Schleif,et al. Design and synthesis of highly potent HIV protease inhibitors with activity against resistant virus. , 2003, Bioorganic & medicinal chemistry letters.
[11] A. Tropsha,et al. Beware of q2! , 2002, Journal of molecular graphics & modelling.
[12] R. Zauhar,et al. Computational studies on HIV-1 protease inhibitors: influence of calculated inhibitor-enzyme binding affinities on the statistical quality of 3D-QSAR CoMFA models. , 2000, Journal of medicinal chemistry.
[13] Tom Tollenaere,et al. SuperSAB: Fast adaptive back propagation with good scaling properties , 1990, Neural Networks.
[14] I. Tetko,et al. QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors , 2015 .
[15] Tarun Jha,et al. Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase‐2 inhibitors , 2013, The Journal of pharmacy and pharmacology.
[17] C. Schiffer,et al. Improving Viral Protease Inhibitors to Counter Drug Resistance. , 2016, Trends in microbiology.
[18] Yuan Chu,et al. HIV protease inhibitors: a review of molecular selectivity and toxicity , 2015, HIV/AIDS.
[19] Giuseppina C. Gini,et al. CORAL: Quantitative structure–activity relationship models for estimating toxicity of organic compounds in rats , 2011, J. Comput. Chem..
[20] Igor V. Tetko,et al. Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices , 2001, J. Chem. Inf. Comput. Sci..
[21] Martyn G. Ford,et al. Unsupervised Forward Selection: A Method for Eliminating Redundant Variables , 2000, J. Chem. Inf. Comput. Sci..
[22] Igor V Tetko,et al. Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform. , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[23] T. Jha,et al. Validated predictive QSAR modeling of N-aryl-oxazolidinone-5-carboxamides for anti-HIV protease activity. , 2010, Bioorganic & medicinal chemistry letters.
[24] Danail Bonchev,et al. Statistical modelling of molecular descriptors in QSAR/QSPR , 2012 .
[25] Pavel Polishchuk,et al. Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future , 2017, J. Chem. Inf. Model..
[26] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[27] P. Darke,et al. HIV-1 protease inhibitors based on hydroxyethylene dipeptide isosteres: an investigation into the role of the P1' side chain on structure-activity. , 1992, Journal of medicinal chemistry.
[28] Arun K. Ghosh,et al. Structure-based design of HIV-1 protease inhibitors: replacement of two amides and a 10 pi-aromatic system by a fused bis-tetrahydrofuran. , 1994, Journal of medicinal chemistry.
[29] 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..
[30] Emilio Benfenati,et al. CORAL: model for no observed adverse effect level (NOAEL) , 2015, Molecular Diversity.
[31] S. Gharaghani,et al. QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors. , 2015, Journal of theoretical biology.
[32] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[33] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[34] M. Jaskólski,et al. Conserved folding in retroviral proteases: crystal structure of a synthetic HIV-1 protease. , 1989, Science.
[35] W. Schleif,et al. P1' oxadiazole protease inhibitors with excellent activity against native and protease inhibitor-resistant HIV-1. , 2004, Bioorganic & medicinal chemistry letters.
[36] Alán Aspuru-Guzik,et al. Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.
[37] G. Nikolic,et al. Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins , 2015, Archiv der Pharmazie.
[38] Cyclic sulfolanes as novel and high affinity P2 ligands for HIV-1 protease inhibitors. , 1993, Journal of medicinal chemistry.
[39] P. Darke,et al. Synthesis and antiviral activity of a series of HIV-1 protease inhibitors with functionality tethered to the P1 or P1' phenyl substituents: X-ray crystal structure assisted design. , 1992, Journal of Medicinal Chemistry.
[40] Alexander Tropsha,et al. Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.
[41] J. Dearden,et al. QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.
[42] A. Saha,et al. Predictive Quantitative Structure Toxicity Relationship Study on Avian Toxicity of Some Diverse Agrochemical Pesticides by Monte Carlo Method: QSTR on Pesticides , 2017 .
[43] W. M. Sanders,et al. L-687,908, a potent hydroxyethylene-containing HIV protease inhibitor. , 1991, Journal of medicinal chemistry.
[44] Sibusiso B. Maseko,et al. I36T↑T mutation in South African subtype C (C-SA) HIV-1 protease significantly alters protease-drug interactions , 2017, Biological chemistry.
[45] L. Kuo,et al. Combinatorial diversification of indinavir: in vivo mixture dosing of an HIV protease inhibitor library. , 2000, Bioorganic & medicinal chemistry letters.
[46] Jiunn H. Lin,et al. Synthesis and activity of novel HIV protease inhibitors with improved potency against multiple PI-resistant viral strains. , 2002, Bioorganic & medicinal chemistry letters.
[47] Vikas Sharma,et al. Purification and characterization of naturally occurring HIV-1 (South African subtype C) protease mutants from inclusion bodies. , 2016, Protein expression and purification.
[48] A. Wensing,et al. Fifteen years of HIV Protease Inhibitors: raising the barrier to resistance. , 2010, Antiviral research.
[49] E A Emini,et al. Benzocycloalkyl amines as novel C-termini for HIV protease inhibitors. , 1991, Journal of medicinal chemistry.
[50] Hongzong Si,et al. QSAR model based on SMILES of inhibitory rate of 2, 3-diarylpropenoic acids on AKR1C3 , 2014 .
[51] T. Govender,et al. Binding Free Energy Calculations of Nine FDA‐approved Protease Inhibitors Against HIV‐1 Subtype C I36T↑T Containing 100 Amino Acids Per Monomer , 2016, Chemical biology & drug design.
[52] Richard A. Lewis,et al. Modern 2D QSAR for drug discovery , 2014 .
[53] Novel HIV-1 protease inhibitors active against multiple PI-resistant viral strains: coadministration with indinavir. , 2003, Bioorganic & medicinal chemistry letters.
[54] Igor V. Tetko,et al. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Comput. Aided Mol. Des..
[55] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[56] B. E. Evans,et al. Cycloalkylpiperazines as HIV-1 protease inhibitors: enhanced oral absorption , 1995 .
[57] Indinavir analogues with blocked metabolism sites as HIV protease inhibitors with improved pharmacological profiles and high potency against PI-resistant viral strains. , 2002, Bioorganic & medicinal chemistry letters.
[58] Igor V. Tetko,et al. Neural Network Studies, 4. Introduction to Associative Neural Networks , 2002, J. Chem. Inf. Comput. Sci..
[59] M. Morris,et al. The Design , 1998 .
[60] Andrey A Toropov,et al. QSAR model for blood-brain barrier permeation. , 2017, Journal of pharmacological and toxicological methods.
[61] Gregory W. Kauffman,et al. QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors , 2001, J. Chem. Inf. Comput. Sci..
[62] P. Darke,et al. The development of cyclic sulfolanes as novel and high-affinity P2 ligands for HIV-1 protease inhibitors. , 1994, Journal of medicinal chemistry.
[63] K. Roy,et al. Further exploring rm2 metrics for validation of QSPR models , 2011 .
[64] Ataul Islam,et al. Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors , 2016 .
[65] K. Roy,et al. Chemometric modeling, docking and in silico design of triazolopyrimidine-based dihydroorotate dehydrogenase inhibitors as antimalarials. , 2010, European journal of medicinal chemistry.
[66] A. Toropova,et al. The Monte Carlo technique as a tool to predict LOAEL. , 2016, European journal of medicinal chemistry.
[67] Yuan Cheng,et al. A combinatorial library of indinavir analogues and its in vitro and in vivo studies. , 2002, Bioorganic & medicinal chemistry letters.
[68] Igor V. Tetko,et al. Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set , 2010, J. Chem. Inf. Model..
[69] Jiunn H. Lin,et al. HIV-1 protease inhibitors with picomolar potency against PI-resistant HIV-1 by modification of the P1' substituent. , 2003, Bioorganic & medicinal chemistry letters.
[70] Aleksandar M Veselinović,et al. Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design. , 2015, Current topics in medicinal chemistry.
[71] E A Emini,et al. Identification of MK-944a: a second clinical candidate from the hydroxylaminepentanamide isostere series of HIV protease inhibitors. , 2000, Journal of medicinal chemistry.
[72] L. Kuo,et al. Combinatorial library of indinavir analogues: replacement for the aminoindanol at P2'. , 2002, Bioorganic & medicinal chemistry letters.
[73] Igor V. Tetko,et al. Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM) , 2013, Molecular informatics.
[74] Supratik Kar,et al. On a simple approach for determining applicability domain of QSAR models , 2015 .
[75] M. Vieth,et al. DoMCoSAR: a novel approach for establishing the docking mode that is consistent with the structure-activity relationship. Application to HIV-1 protease inhibitors and VEGF receptor tyrosine kinase inhibitors. , 2000, Journal of medicinal chemistry.
[76] Jelena V. Zivkovic,et al. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors , 2015, Comput. Biol. Medicine.
[77] Gerhard Klebe,et al. Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures , 1994, J. Chem. Inf. Comput. Sci..
[78] L. Palmisano,et al. A brief history of antiretroviral therapy of HIV infection: success and challenges. , 2011, Annali dell'Istituto superiore di sanita.
[79] M Pastor,et al. Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design. , 1998, Journal of medicinal chemistry.
[80] Jiunn H. Lin,et al. The design, synthesis and evaluation of novel HIV-1 protease inhibitors with high potency against PI-resistant viral strains. , 2003, Bioorganic & medicinal chemistry letters.
[81] T. Halgren,et al. A priori prediction of activity for HIV-1 protease inhibitors employing energy minimization in the active site. , 1995, Journal of medicinal chemistry.
[82] David Dunn,et al. Demonstration of Sustained Drug-Resistant Human Immunodeficiency Virus Type 1 Lineages Circulating among Treatment-Naïve Individuals , 2009, Journal of Virology.
[83] Gregg D. Wilensky,et al. Neural Network Studies , 1993 .
[84] W. Elshemey,et al. Fullerene derivative as anti-HIV protease inhibitor: molecular modeling and QSAR approaches. , 2012, Mini reviews in medicinal chemistry.
[85] Jim Euchner. Design , 2014, Catalysis from A to Z.
[86] W. Schleif,et al. Synthesis of novel HIV protease inhibitors (PI) with activity against PI-resistant virus. , 2007, Bioorganic & medicinal chemistry letters.
[87] Danishuddin,et al. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. , 2016, Drug discovery today.
[88] Igor V. Tetko,et al. Associative Neural Network , 2002, Neural Processing Letters.
[89] P. Darke,et al. L-735,524: the design of a potent and orally bioavailable HIV protease inhibitor. , 1994, Journal of medicinal chemistry.
[90] John B. O. Mitchell. Machine learning methods in chemoinformatics , 2014, Wiley interdisciplinary reviews. Computational molecular science.
[91] Novel nonpeptidic inhibitors of HIV-1 protease obtained via a new multicomponent chemistry strategy. , 2004, Bioorganic & medicinal chemistry letters.
[92] Jerzy Leszczynski,et al. QSAR model as a random event: A case of rat toxicity. , 2015, Bioorganic & medicinal chemistry.
[93] E A Emini,et al. 3-Tetrahydrofuran and pyran urethanes as high-affinity P2-ligands for HIV-1 protease inhibitors. , 1993, Journal of medicinal chemistry.
[94] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[95] A. Toropova,et al. QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. , 2014, European journal of medicinal chemistry.