In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches.
暂无分享,去创建一个
Hao Zhu | Xiaoli Zhao | Bing Yan | Wenyi Wang | Alexander Sedykh | Xiliang Yan | Alexander Sedykh | B. Yan | Hao Zhu | Wenyi Wang | Xiliang Yan | Xiaoli Zhao
[1] Vincent M Rotello,et al. Tunable inhibition and denaturation of alpha-chymotrypsin with amino acid-functionalized gold nanoparticles. , 2005, Journal of the American Chemical Society.
[2] Bin Zhao,et al. Elucidation of the Molecular Determinants for Optimal Perfluorooctanesulfonate Adsorption Using a Combinatorial Nanoparticle Library Approach. , 2017, Environmental science & technology.
[3] D. Winkler,et al. Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries , 2015, Nano Research.
[4] M. Hormozi-Nezhad,et al. Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis , 2015 .
[5] Jim E Riviere,et al. An index for characterization of nanomaterials in biological systems. , 2010, Nature nanotechnology.
[6] A. Tropsha,et al. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles , 2016, Nanotoxicology.
[7] Jerzy Leszczynski,et al. Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides , 2014, Environmental Science and Pollution Research.
[8] Morteza Mahmoudi,et al. Exploring Cellular Interactions of Liposomes Using Protein Corona Fingerprints and Physicochemical Properties. , 2016, ACS nano.
[9] Jerzy Leszczynski,et al. From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. , 2014, Nanoscale.
[10] Dan Peer,et al. Nanoparticle hydrophobicity dictates immune response. , 2012, Journal of the American Chemical Society.
[11] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[12] Danail Hristozov,et al. Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs , 2016, Nanotoxicology.
[13] V. Rotello,et al. Modulating Pharmacokinetics, Tumor Uptake and Biodistribution by Engineered Nanoparticles , 2011, PloS one.
[14] David A Winkler,et al. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. , 2016, Toxicology and applied pharmacology.
[15] Hainan Sun,et al. Induction of oxidative stress and sensitization of cancer cells to paclitaxel by gold nanoparticles with different charge densities and hydrophobicities. , 2018, Journal of materials chemistry. B.
[16] Jerzy Leszczynski,et al. Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose. , 2017, Nanoscale.
[17] Marlene T. Kim,et al. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling , 2015, Pharmaceutical Research.
[18] Stefan Seeger,et al. Industrial production quantities and uses of ten engineered nanomaterials in Europe and the world , 2012, Journal of Nanoparticle Research.
[19] Yi Zhang,et al. Repeated carbon nanotube administrations in male mice cause reversible testis damage without affecting fertility , 2010, Nature Nanotechnology.
[20] Arnaud Magrez,et al. In vitro investigation of the cellular toxicity of boron nitride nanotubes. , 2011, ACS nano.
[21] Jian Wang,et al. Nano(Q)SAR: Challenges, pitfalls and perspectives , 2015, Nanotoxicology.
[22] Hongyu Zhou,et al. A nano-combinatorial library strategy for the discovery of nanotubes with reduced protein-binding, cytotoxicity, and immune response. , 2008, Nano letters.
[23] Shikha Gupta,et al. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials , 2014 .
[24] Christy L. Haynes,et al. Functional assessment of metal oxide nanoparticle toxicity in immune cells. , 2010, ACS nano.
[25] M. Natália D. S. Cordeiro,et al. Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory , 2017, Nanotoxicology.
[26] Paul R. Gerber,et al. MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry , 1995, J. Comput. Aided Mol. Des..
[27] Rong Liu,et al. Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries. , 2013, Small.
[28] Daniel P Russo,et al. Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. , 2017, ACS nano.
[29] Sung Tae Kim,et al. Regulation of Macrophage Recognition through the Interplay of Nanoparticle Surface Functionality and Protein Corona. , 2016, ACS nano.
[30] Andrew P. Worth,et al. QSAR modeling of nanomaterials. , 2011, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.
[31] Marlene T. Kim,et al. Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches , 2013, Pharmaceutical Research.
[32] Ruili Huang,et al. Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data , 2015, Environmental health perspectives.
[33] Julie Clark,et al. Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening , 2013, J. Chem. Inf. Model..
[34] Hongmao Sun,et al. A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption , 2004, J. Chem. Inf. Model..
[35] Alexander Tropsha,et al. Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..
[36] Bo Yan,et al. Fabrication of Corona-Free Nanoparticles with Tunable Hydrophobicity , 2014, ACS nano.
[37] Timo Laaksonen,et al. Stability and electrostatics of mercaptoundecanoic acid-capped gold nanoparticles with varying counterion size. , 2006, Chemphyschem : a European journal of chemical physics and physical chemistry.
[38] M. Barkley,et al. Toward understanding tryptophan fluorescence in proteins. , 1998, Biochemistry.
[39] Robert C. Glen,et al. Novel Methods for the Prediction of logP, pKa, and logD , 2002, J. Chem. Inf. Comput. Sci..
[40] S. Hou,et al. The Interplay of Size and Surface Functionality on the Cellular Uptake of Sub-10 nm Gold Nanoparticles. , 2015, ACS nano.
[41] V. Pande,et al. Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece. , 2007, Journal of molecular biology.
[42] Julio C. Facelli,et al. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles , 2016, Comput. Methods Programs Biomed..
[43] K. L. D. M. Weerawardene,et al. Quantum Mechanical Studies of Large Metal, Metal Oxide, and Metal Chalcogenide Nanoparticles and Clusters. , 2015, Chemical reviews.
[44] Feng Luan,et al. Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. , 2014, Nanoscale.
[45] Andrew Emili,et al. Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles. , 2014, ACS nano.
[46] Hao Zhu,et al. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers , 2014, Journal of Computer-Aided Molecular Design.
[47] Bo Yan,et al. Surface Charge Controls the Suborgan Biodistributions of Gold Nanoparticles. , 2016, ACS nano.
[48] Vincent M. Rotello,et al. Enzyme-amplified array sensing of proteins in solution and in biofluids. , 2010, Journal of the American Chemical Society.
[49] Louis H. Haber,et al. Determination of the Surface Charge Density of Colloidal Gold Nanoparticles Using Second Harmonic Generation , 2015 .
[50] Jerzy Leszczynski,et al. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies , 2015, Nanotoxicology.
[51] Iosif I. Vaisman,et al. Delaunay Tessellation of Proteins: Four Body Nearest-Neighbor Propensities of Amino Acid Residues , 1996, J. Comput. Biol..
[52] Andrey A Toropov,et al. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. , 2015, Chemosphere.
[53] Ran Chen,et al. Quantification of nanoparticle pesticide adsorption: computational approaches based on experimental data , 2016, Nanotoxicology.
[54] T. Puzyn,et al. Toward the development of "nano-QSARs": advances and challenges. , 2009, Small.
[55] D. Astruc,et al. Gold nanoparticles: assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology. , 2004, Chemical reviews.
[56] W. Chan,et al. Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties. , 2015, Nanoscale.
[57] Bing Yan,et al. Enhancing cell recognition by scrutinizing cell surfaces with a nanoparticle array. , 2011, Journal of the American Chemical Society.
[58] A. Tropsha,et al. Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. , 2006, Journal of medicinal chemistry.
[59] Bo Yan,et al. Dual-Mode Mass Spectrometric Imaging for Determination of in Vivo Stability of Nanoparticle Monolayers. , 2017, ACS nano.
[60] Yang Li,et al. Perturbation of physiological systems by nanoparticles. , 2014, Chemical Society reviews.
[61] Arafeh Bigdeli,et al. Towards defining new nano-descriptors: extracting morphological features from transmission electron microscopy images , 2014 .
[62] Kyle A. Beauchamp,et al. Quantitative comparison of villin headpiece subdomain simulations and triplet–triplet energy transfer experiments , 2011, Proceedings of the National Academy of Sciences.
[63] Jerzy Leszczynski,et al. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. , 2011, Nature nanotechnology.
[64] Tarmo Tamm,et al. Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors. , 2018, Nanoscale.
[65] Lianhui Wang,et al. Intracellular Adenosine Triphosphate Deprivation through Lanthanide-Doped Nanoparticles. , 2015, Journal of the American Chemical Society.
[66] Jerzy Leszczynski,et al. SMILES‐based optimal descriptors: QSAR analysis of fullerene‐based HIV‐1 PR inhibitors by means of balance of correlations , 2009, J. Comput. Chem..
[67] A. Tropsha,et al. Quantitative nanostructure-activity relationship modeling. , 2010, ACS nano.
[68] Craig A. Poland,et al. Zeta potential and solubility to toxic ions as mechanisms of lung inflammation caused by metal/metal oxide nanoparticles. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.
[69] R. Weissleder,et al. Modeling biological activities of nanoparticles. , 2012, Nano letters.
[70] David Rejeski,et al. Nanotechnology in the real world: Redeveloping the nanomaterial consumer products inventory , 2015, Beilstein journal of nanotechnology.
[71] Bradley Duncan,et al. Targeting bacterial biofilms via surface engineering of gold nanoparticles. , 2015, RSC advances.
[72] Vincent M Rotello,et al. The role of surface functionality on acute cytotoxicity, ROS generation and DNA damage by cationic gold nanoparticles. , 2010, Small.
[73] L Mädler,et al. Parametrization of nanoparticles: development of full-particle nanodescriptors. , 2016, Nanoscale.
[74] Hao Zhu,et al. Universal nanohydrophobicity predictions using virtual nanoparticle library , 2019, Journal of Cheminformatics.
[75] G. Caracciolo,et al. Nanoparticles-cell association predicted by protein corona fingerprints. , 2016, Nanoscale.
[76] Bin Zhao,et al. Modulation of Carbon Nanotubes' Perturbation to the Metabolic Activity of CYP3A4 in the Liver , 2016 .
[77] A. Nel,et al. Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. , 2011, Small.
[78] Natalia Barkalina,et al. Nanotechnology in reproductive medicine: emerging applications of nanomaterials. , 2014, Nanomedicine : nanotechnology, biology, and medicine.
[79] Xue Z. Wang,et al. (Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review , 2015 .
[80] Kyeongjae Cho,et al. Developing Descriptors To Predict Mechanical Properties of Nanotubes , 2013, J. Chem. Inf. Model..
[81] Serdar Durdagi,et al. Computational design of novel fullerene analogues as potential HIV-1 PR inhibitors: Analysis of the binding interactions between fullerene inhibitors and HIV-1 PR residues using 3D QSAR, molecular docking and molecular dynamics simulations. , 2008, Bioorganic & medicinal chemistry.
[82] Alexander Tropsha,et al. Chembench: a cheminformatics workbench , 2010, Bioinform..