Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials

NanoImpact - Tập 1 - Trang 60-64 - 2016
Alla P. Toropova1, Andrey A. Toropov1, Serena Manganelli1, Caterina Leone1, Diego Baderna1, Emilio Benfenati1, Roberto Fanelli1
1IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy

Tài liệu tham khảo

Buzea, 2007, Nanomaterials and nanoparticles: sources and toxicity, Biointerphases, 2, MR17, 10.1116/1.2815690 Duchowicz, 2015, QSPR studies on refractive indices of structurally heterogeneous polymers, Chemom. Intell. Lab. Syst., 140, 86, 10.1016/j.chemolab.2014.11.008 Gajewicz, 2015, Towards understanding mechanisms governing cytotoxicity of metaloxides nanoparticles: hints from nano-qsar studies, Nanotoxicology, 9, 313, 10.3109/17435390.2014.930195 CORAL, http://www.insilico.eu/coral (Accessed Feb 5, 2016) Ibezim, 2012, QSAR on aryl-piperazine derivatives with activity on malaria, Chemom. Intell. Lab. Syst., 110, 81, 10.1016/j.chemolab.2011.10.002 Kar, 2016, Extrapolating between toxicity endpoints of metal oxide nanoparticles: predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with nano-QTTR, Ecotoxicol. Environ. Saf., 126, 238, 10.1016/j.ecoenv.2015.12.033 Kleandrova, 2015, In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect, Mini-Rev. Med. Chem., 15, 677, 10.2174/1389557515666150219143604 Lewinski, 2008, Cytotoxicity of nanopartides, Small, 4, 26, 10.1002/smll.200700595 Manganelli, 2016, QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles, Chemosphere, 144, 995, 10.1016/j.chemosphere.2015.09.086 Melagraki, 2013, Enalos KNIME nodes: exploring corrosion inhibition of steel in acidic medium, Chemom. Intell. Lab. Syst., 123, 9, 10.1016/j.chemolab.2013.02.003 Ojha, 2011, Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection, Chemom. Intell. Lab. Syst., 109, 146, 10.1016/j.chemolab.2011.08.007 Organisation For Economic Co-Operation And Development (OECD) Puzyn, 2011, Using nano-QSAR to predict the cytotoxicity of metaloxide nanoparticles, Nat. Nanotechnol., 6, 175, 10.1038/nnano.2011.10 Ray, 2009, Toxicity and environmental risks of nanomaterials: challenges and future needs, J. Environ. Sci. Health., Part C Environ. Carcinog. Ecotoxicol. Rev., 27, 1, 10.1080/10590500802708267 Scotti, 2014, Docking and PLS studies on a set of thiophenes RNA polymerase inhibitors against Staphylococcus aureus, Curr. Top. Med. Chem., 14, 64, 10.2174/1568026613666131113151347 Singh, 2014, Nano-QSAR modeling for predicting biological activity of diverse nanomaterials, RSC Adv., 4, 13215, 10.1039/C4RA01274G Speck-Planche, 2015, Multi-target QSAR approaches for modeling protein inhibitors. Simultaneous prediction of activities against biomacromolecules present in Gram-negative bacteria, Curr. Top. Med. Chem., 15, 1801, 10.2174/1568026615666150506144814 Toropov, 2015, Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes, Chemosphere, 124, 40, 10.1016/j.chemosphere.2014.10.067 Toropov, 2015, Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions, Chemosphere, 139, 18, 10.1016/j.chemosphere.2015.05.042 Toropov, 2012, Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli, Chemosphere, 89, 1098, 10.1016/j.chemosphere.2012.05.077 Toropov, 2014, Comprehension of drug toxicity: software and databases, Comput. Biol. Med., 45, 20, 10.1016/j.compbiomed.2013.11.013 Toropov, 2015, Use of Quasi-SMILES and Monte Carlo optimization to develop quantitative feature property/activity relationships (QFPR/QFAR) for nanomaterials, Curr. Top. Med. Chem., 15, 1837, 10.2174/1568026615666150506152000 Toropova, 2015, Mutagenicity: QSAR-quasi-QSAR-nano-QSAR, Mini Rev. Med. Chem., 15, 608, 10.2174/1389557515666150219121652 Toropova, 2015, Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions, Chemosphere, 139, 18, 10.1016/j.chemosphere.2015.05.042 Veselinović, 2015, In silico prediction of the β-cyclodextrin complexation based on Monte Carlo method, Int. J. Pharm., 495, 404, 10.1016/j.ijpharm.2015.08.078 Veselinović, 2015, Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins, Arch. Pharm., 348, 62, 10.1002/ardp.201400259 Weininger, 1988, SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules, J. Chem. Inf. Comput. Sci., 28, 31, 10.1021/ci00057a005 Weininger, 1990, Smiles. 3. Depict. Graphical depiction of chemical structures, J. Chem. Inf. Comput. Sci., 30, 237, 10.1021/ci00067a005 Weininger, 1989, SMILES: 2. algorithm for generation of unique SMILES notation, J. Chem. Inf. Comput. Sci., 29, 97, 10.1021/ci00062a008