“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling

Gajewicz, 2012, Advancing risk assessment of engineered nanomaterials: application of computational approaches, Adv. Drug Deliv. Rev., 64, 1663, 10.1016/j.addr.2012.05.014 Buzea, 2007, Nanomaterials and nanoparticles: sources and toxicity, Biointerphases, 2, MR17, 10.1116/1.2815690 Manke, 2013, Mechanisms of nanoparticle-induced oxidative stress and toxicity, BioMed. Res. Int., 1, 10.1155/2013/942916 Lynch, 2014, A strategy for grouping of nanomaterials based on key physico-chemical descriptors as a basis for safer-by-design NMs, Nano Today, 9, 266, 10.1016/j.nantod.2014.05.001 Tantra, 2014, Nano (Q) SAR: Challenges, pitfalls and perspectives, Nanotoxicology, 1 Winkler, 2014, Modelling and predicting the biological effects of nanomaterials, SAR QSAR Environ. Res., 25, 161, 10.1080/1062936X.2013.874367 Puzyn, 2009, Toward the development of "nano-QSAR": Advances and challenges, Small, 5, 2494, 10.1002/smll.200900179 Fourches, 2010, Quantitative nanostructure–activity relationship modeling, ACS Nano, 4, 5703, 10.1021/nn1013484 Puzyn, 2011, Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles, Nat. Nanotechnol., 6, 175, 10.1038/nnano.2011.10 Fourches, 2011, Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles, Comb. Chem. High Throughput Screen., 14, 217, 10.2174/138620711794728743 Epa, 2012, Modeling biological activities of nanoparticles, Nano Lett., 12, 5808, 10.1021/nl303144k Zhang, 2012, Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation, ACS Nano, 6, 4349, 10.1021/nn3010087 Gajewicz, 2014, Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies, Nanotoxicology, 1 Kar, 2014, Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells, Toxicol. In Vitro, 28, 600, 10.1016/j.tiv.2013.12.018 Kar, 2014, Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach, Ecotoxicol. Environ. Saf., 107, 162, 10.1016/j.ecoenv.2014.05.026 Winkler, 2013, Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential, Toxicology, 313, 15, 10.1016/j.tox.2012.11.005 Singh, 2014, Nano-QSAR modeling for predicting biological activity of diverse nanomaterials, RSC Adv., 4, 13215, 10.1039/C4RA01274G I.N.C. Minitab, 2000, MINITAB statistical software, Minitab Release, 13 Roy, 2011, On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design, Comb. Chem. High Throughput Screen., 14, 450, 10.2174/138620711795767893 Roy, 2012, Comparative studies on some metrics for external validation of QSPR models, J. Chem. Inf. Model., 52, 396, 10.1021/ci200520g Golbraikh, 2002, Beware of q 2!, J. Mol. Graph. Model., 20, 269, 10.1016/S1093-3263(01)00123-1 Aher, 2014, First report on two-fold classification of plasmodium falciparum carbonic anhydrase inhibitors using QSAR modeling approaches, Comb. Chem. High Throughput Screen., 17, 745, 10.2174/1386207317666140828123920 Golbraikh, 2014, Data set modelability by QSAR, J. Chem. Inf. Model., 54, 1, 10.1021/ci400572x Golmohammadi, 2012, Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine, Eur. J. Pharm. Sci., 47, 421, 10.1016/j.ejps.2012.06.021 Ballabio, 2014, A novel variable reduction method adapted from space-filling designs, Chemom. Intell. Lab. Syst., 136, 147, 10.1016/j.chemolab.2014.05.010 De Maesschalck, 2000, The Mahalanobis distance, Chemom. Intell. Lab. Syst., 50, 1, 10.1016/S0169-7439(99)00047-7 Martens, 1992 Rousseeuw, 2005 Shenk, 1991, Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy, Crop Sci., 31, 469, 10.2135/cropsci1991.0011183X003100020049x Jouan-Rimbaud, 1997, Characterisation of the representativity of selected sets of samples in multivariate calibration and pattern recognition, Anal. Chim. Acta, 350, 149, 10.1016/S0003-2670(97)00296-1 Jouan-Rimbaud, 1998, Determination of the representativity between two multidimensional data sets by a comparison of their structure, Chemom. Intell. Lab. Syst., 40, 129, 10.1016/S0169-7439(98)00005-7 Wu, 1996, Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data, Anal. Chim. Acta, 329, 257, 10.1016/0003-2670(96)00142-0 Derde, 1986, UNEQ: a disjoint modelling technique for pattern recognition based on normal distribution, Anal. Chim. Acta, 184, 33, 10.1016/S0003-2670(00)86468-5 Coomans, 1984, Use of a microcomputer for the definition of multivariate confidence regions in medical diagnosis based on clinical laboratory profiles, Comput. Biomed. Res., 17, 1, 10.1016/0010-4809(84)90002-8 Saptoro, 2012, A modified Kennard-Stone algorithm for optimal division of data for developing artificial neural network models, Chem. Prod. Process. Model., 7 Park, 2009, A simple and fast algorithm for K-medoids clustering, Expert Syst. Appl., 36, 3336, 10.1016/j.eswa.2008.01.039 Venkatasubramanian, 1998 Roy, 2013, Some case studies on application of rm2 metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data, J. Comput. Chem., 34, 1071, 10.1002/jcc.23231 Roy, 2008, On some aspects of variable selection for partial least squares regression models, QSAR Comb. Sci., 27, 302, 10.1002/qsar.200710043 Kaufman, 1987 Yan, 2014, A combinational strategy of model disturbance and outlier comparison to define applicability domain in quantitative structural activity relationship, Mol. Inf., 33, 503, 10.1002/minf.201300161