Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

References

• Adeleye AS, Conway JR, Garner K, Huang YX, Su YM, Keller AA. 2016. Engineered nanomaterials for water treatment and remediation: costs, benefits, and applicability. Chem Eng J 286:640–62
   Web of Science ®Google Scholar
• Andres C, Hutter MC. 2006. CNS permeability of drugs predicted by a decision tree. Qsar Comb Sci 25:305–9
   Google Scholar
• Arena VC, Sussman NB, Mazumdar S, Yu S, Macina OT. 2004. The utility of structure–activity relationship (SAR) models for prediction and covariate selection in developmental toxicity: comparative analysis of logistic regression and decision tree models. SAR QSAR Environ Res 15:1–18
   PubMed Web of Science ®Google Scholar
• Ariew R. 1976. Ockham's Razor: A Historical and Philosophical Analysis of Ockham's Principle of Parsimony. Champaign-Urbana: University of Illinois Press
   Google Scholar
• Bakhtyari GN, Bakhtyari GA, Benfenati E, Cronin M, Rasulev B, Leszczynski J. 2014. Prediction of Genotoxicity of Nano Metal Oxides by Computational Methods: A New Decision Tree QSAR Model. Environmental and Molecular Mutagenesis. Hoboken, NJ, USA: Wiley-Blackwell, S43
   Google Scholar
• Biffi S, Voltan R, Rampazzo E, Prodi L, Zauli G, Secchiero P. 2015. Applications of nanoparticles in cancer medicine and beyond: optical and multimodal in vivo imaging, tissue targeting and drug delivery. Expert Opin Drug Deliv 12:1837–49
   PubMed Web of Science ®Google Scholar
• Bigdeli A, Hormozi-Nezhad MR, Parastar H. 2015. Using nano-QSAR to determine the most responsible factor (s) in gold nanoparticle exocytosis. RSC Adv 5:57030–7
   Web of Science ®Google Scholar
• Bumbudsanpharoke N, Choi J, Ko S. 2015. Applications of nanomaterials in food packaging. J Nanosci Nanotechnol 15:6357–72
   PubMed Web of Science ®Google Scholar
• Buontempo FV, Wang XZ, Mwense M, Horan N, Young A, Osborn D. 2005. Genetic programming for the induction of decision trees to model ecotoxicity data. J Chem Inf Model 45:904–12
   PubMed Web of Science ®Google Scholar
• Burden FR, Winkler DA. 2009. Optimal sparse descriptor selection for QSAR using bayesian methods. Qsar Comb Sci 28:645–53
   Google Scholar
• Burello E, Worth AP. 2011. A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles. Nanotoxicology 5:228–35
   PubMed Web of Science ®Google Scholar
• Burello E, Worth AP. 2011. QSAR modeling of nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3:298–306
   PubMed Web of Science ®Google Scholar
• Chau YT, Yap CW. 2012. Quantitative nanostructure–activity relationship modelling of nanoparticles. RSC Adv 2:8489–96
   Web of Science ®Google Scholar
• DeLisle RK, Dixon SL. 2004. Induction of decision trees via evolutionary programming. J Chem Inform Comput Sci 44:862–70
   PubMedGoogle Scholar
• Durdagi S, Mavromoustakos T, Chronakis N, Papadopoulos MG. 2008. 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. Bioorg Med Chem 16:9957–74
   PubMed Web of Science ®Google Scholar
• eNanoMapper. Journal [Online]. Accessed on July 28 2015
   Google Scholar
• Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA. 2012. Modeling biological activities of nanoparticles. Nano Lett 12:5808–12
   PubMed Web of Science ®Google Scholar
• Fahmy T. 1993. XLSTAT-Software, Version 10. Paris, France: Addinsoft
   Google Scholar
• Fourches D, Pu D, Li L, Zhou H, Mu Q, Su G, et al. 2016. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles. Nanotoxicology 10:374–83
   PubMed Web of Science ®Google Scholar
• Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY, Mumper RJ, Tropsha A. 2010. Quantitative nanostructure-activity relationship modeling. ACS Nano 4:5703–12
   PubMed Web of Science ®Google Scholar
• Fourches D, Pu D, Tropsha A. 2011. Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles. Combin Chem High Throughput Screen 14:217–25
   PubMed Web of Science ®Google Scholar
• Gajewicz A, Rasulev B, Dinadayalane TC, Urbaszek P, Puzyn T, Leszczynska D, Leszczynski J. 2012. Advancing risk assessment of engineered nanomaterials: application of computational approaches. Adv Drug Deliv Rev 64:1663–93
   PubMed Web of Science ®Google Scholar
• Gajewicz A, Schaeublin N, Rasulev B, Hussain S, Leszczynska D, Puzyn T, Leszczynski J. 2015. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies. Nanotoxicology 9:313–25
   PubMed Web of Science ®Google Scholar
• Garduno-Balderas LG, Urrutia-Ortega IM, Medina-Reyes EI, Chirino YI. 2015. Difficulties in establishing regulations for engineered nanomaterials and considerations for policy makers: avoiding an unbalance between benefits and risks. J Appl Toxicol 35:1073–85
   PubMed Web of Science ®Google Scholar
• Han L, Wang Y, Bryant SH. 2008. Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem. BMC Bioinformatics 9:401
   PubMed Web of Science ®Google Scholar
• Kar S, Gajewicz A, Puzyn T, Roy K. 2014. Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells. Toxicology In Vitro 28:600–6
   PubMed Web of Science ®Google Scholar
• Le TC, Yan B, Winkler DA. 2015. Robust prediction of personalized cell recognition from a cancer population by a dual targeting nanoparticle library. Adv Funct Mater 25:6927–35
   Web of Science ®Google Scholar
• Le TC, Winkler DA, 2016. Discovery and optimization of materials using evolutionary approaches. Chem Rev, submitted
   Google Scholar
• Le TC, Conn CE, Burden FR, Winkler DA. 2013. Computational modeling and prediction of the complex time-dependent phase behavior of lyotropic liquid crystals under in meso crystallization conditions. Cryst Growth Des 13:1267–76
   Web of Science ®Google Scholar
• Liu Y, Winkler DA, Epa VC, Zhang B, Yan B. 2014. Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries. Nano Res 8:1293–308
   Web of Science ®Google Scholar
• Liu R, Zhang HY, Ji ZX, Rallo R, Xia T, Chang CH, et al. 2013. Development of structure–activity relationship for metal oxide nanoparticles. Nanoscale 5:5644–53
   PubMed Web of Science ®Google Scholar
• Liu R, Rallo R, Weissleder R, Tassa C, Shaw S, Cohen Y. 2013. Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries. Small 9:1842–52
   PubMed Web of Science ®Google Scholar
• Liu R, Rallo R, George S, Ji Z, Nair S, Nel AE, Cohen Y. 2011. Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. Small 7:1118–26
   PubMed Web of Science ®Google Scholar
• Liu R, Jiang W, Walkey CD, Chan WC, Cohen Y. 2015. Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties. Nanoscale 7:9664–75
   PubMed Web of Science ®Google Scholar
• Lynch I, Weiss C, Valsami-Jones E. 2014. A strategy for grouping of nanomaterials based on key physico-chemical descriptors as a basis for safer-by-design NMs. Nano Today 9:266–70
   Web of Science ®Google Scholar
• Ma JS, Sheridan RP, Liaw A, Dahl GE, Svetnik V. 2015. Deep neural nets as a method for quantitative structure–activity relationships. J Chem Inf Model 55:263–74
   PubMed Web of Science ®Google Scholar
• Ma CY, Buontempo FV, Wang XZ. 2008. Inductive data mining: automatic generation of decision trees from data for QSAR modelling and process historical data analysis. Computer Aided Chem Eng 25:581–6
   Google Scholar
• Mauri A, Consonni V, Pavan M, Todeschini R. 2006. Dragon software: an easy approach to molecular descriptor calculations. Match 56:237–48
   Google Scholar
• Nel A, Xia T, Mädler L, Li N. 2006. Toxic potential of materials at the nanolevel. Science 311:622–7
   PubMed Web of Science ®Google Scholar
• Oh N, Park JH. 2014. Surface chemistry of gold nanoparticles mediates their exocytosis in macrophages. ACS Nano 8:6232–41
   PubMed Web of Science ®Google Scholar
• Oksel C, Ma C, Wang X. 2015. Current situation on the availability of nanostructure-biological activity data. SAR QSAR Environ Res 26:79–94
   PubMed Web of Science ®Google Scholar
• Oksel C, Ma CY, Liu JJ, Wilkins T, Wang XZ. 2015. (Q) SAR modelling of nanomaterial toxicity: a critical review. Particuology 21:1–19
   Web of Science ®Google Scholar
• Pathakoti K, Huang MJ, Watts JD, He X, Hwang HM. 2014. Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles. J Photochem Photobiol B: Biol 130:234–40
   PubMed Web of Science ®Google Scholar
• Poater A, Saliner AG, Solà M, Cavallo L, Worth AP. 2010. Computational methods to predict the reactivity of nanoparticles through structure–property relationships. Expert Opin Drug Del 7:295–305
   PubMed Web of Science ®Google Scholar
• Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari T, Michalkova A, et al. 2011. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol 6:175–8
   PubMed Web of Science ®Google Scholar
• Puzyn T, Leszczynska D, Leszczynski J. 2009. Toward the development of “ARs’: advances and challenges”. Small 5:2494–509
   PubMed Web of Science ®Google Scholar
• Quinlan JR. 1986. Induction of decision trees. Mach Learn 1:81–6
   Google Scholar
• Robinson RLM, Cronin MT, Richarz AN, Rallo R. 2015. An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology. Beilst J Nanotechnol 6:1978–99
   PubMed Web of Science ®Google Scholar
• Sajid M, Ilyas M, Basheer C, Tariq M, Daud M, Baig N, Shehzad F. 2015. Impact of nanoparticles on human and environment: review of toxicity factors, exposures, control strategies, and future prospects. Environ Sci Pollut Res Int 22:4122–43
   PubMed Web of Science ®Google Scholar
• Sayes C, Ivanov I. 2010. Comparative study of predictive computational models for nanoparticle-induced cytotoxicity. Risk Anal 30:1723–34
   PubMed Web of Science ®Google Scholar
• Singh KP, Gupta S. 2014. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. RSC Adv 4:13215–30
   Web of Science ®Google Scholar
• Sizochenko N, Rasulev B, Gajewicz A, Mokshyna E, Kuz'min VE, Leszczynski J, Puzyn T. 2015. Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models. RSC Adv 5:77739–45
   Web of Science ®Google Scholar
• Sussman N, Arena V, Yu S, Mazumdar S, Thampatty B. 2003. Decision tree SAR models for developmental toxicity based on an FDA/TERIS database. SAR QSAR Environ Res 14:83–96
   PubMed Web of Science ®Google Scholar
• Turabekova MA, Rasulev BF. 2004. A QSAR toxicity study of a series of alkaloids with the lycoctonine skeleton. Molecules 9:1194–207
   PubMed Web of Science ®Google Scholar
• Wang X, Buontempo F, Young A, Osborn D. 2006. Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints. SAR QSAR Environ Res 17:451–71
   PubMed Web of Science ®Google Scholar
• Wang XZ, Yang Y, Li RF, McGuinnes C, Adamson J, Megson IL, Donaldson K. 2014. Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles. Nanotoxicology 8:465–76
   PubMed Web of Science ®Google Scholar
• Weissleder R, Kelly K, Sun EY, Shtatland T, Josephson L. 2005. Cell-specific targeting of nanoparticles by multivalent attachment of small molecules. Nat Biotechnol 23:1418–23
   PubMed Web of Science ®Google Scholar
• Winkler DA, Mombelli E, Pietroiusti A, Tran L, Worth A, Fadeel B, McCall MJ. 2012. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. Toxicology 313:15–23
   PubMed Web of Science ®Google Scholar
• Winkler DA. 2015. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. Invited paper, Toxicol Appl Pharmacol. [Epub ahead of print]. doi:10.1016/j.taap.2015.12.016
   Google Scholar
• Wold S, Eriksson L. 1995. Statistical validation of QSAR results. In: Waterbeemd Hvd, ed. Chemometrics Methods in Molecular Design (Methods and principles in medicinal chemistry Vol 2). Weinheim (Germany): Wiley-VCH Verlag GmbH, 309–38
   Google Scholar
• Zhang H, Ji Z, Xia T, Meng H, Low-Kam C, Liu R, et al. 2012. Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 6:4349–68
   PubMed Web of Science ®Google Scholar