Development of computational methods for the prediction of material properties

Varsou, Dimitra-Danai; Βάρσου, Δήμητρα-Δανάη

dc.contributor.author	Varsou, Dimitra-Danai	el
dc.contributor.author	Βάρσου, Δήμητρα-Δανάη	en
dc.date.accessioned	2021-11-26T08:45:55Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54101
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21799
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/gr/	*
dc.subject	nanoinformatics	en
dc.subject	read-across	en
dc.subject	predictive modelling	en
dc.subject	safety-by-design	en
dc.subject	νανοπληροφορική	el
dc.subject	προβλεπτικά μοντέλα	el
dc.subject	ασφάλεια στο στάδιο του σχεδιασμού	el
dc.subject	συγκριτικό πλαίσιο read-across	el
dc.subject	engineered nanomaterials	en
dc.subject	διαδικτυακές εφαρμογές	el
dc.title	Development of computational methods for the prediction of material properties	en
dc.title	Ανάπτυξη υπολογιστικών μεθόδων για την πρόβλεψη ιδιοτήτων υλικών	el
heal.type	doctoralThesis
heal.classification	Machine learning	en
heal.classification	Nanoinformatics	en
heal.dateAvailable	2022-11-25T22:00:00Z
heal.language	en
heal.access	embargo
heal.recordProvider	ntua	el
heal.publicationDate	2021-07-02
heal.abstract	The main objective of this PhD program is the development of innovative computational read-across methods for predicting engineered nanomaterial (ENM) properties (with emphasis to toxicity-related endpoints), based on experimental data. The read-across methods aim at determining neighbours (similar samples) to the query ENM in a dataset of ENMs with known properties and creating groups of related substances that have similar biological activity or toxic response. An important step in all the developed methodologies is the selection of the properties that are relevant to the endpoint of interest, to reduce the dimensionality of the models, avoid over-fitting and generate interpretable models. The automation of all the modelling parameters, is a key goal in this research project, and the proposed methodologies require the minimum information from the users to produce valid and robust read-across models. Special emphasis was given in the making of the models developed in this program available through repositories or via user-friendly web applications. Implementation of the models as web tools supports their dissemination and actual use by all stakeholders in real-life applications. To begin with, a novel read-across methodology, related to the prediction of ENMs toxicity was developed. The method selects the most important variables and defines the neighbouring area around the target ENM, using single or multiple similarity criteria. The similarity criteria depend on the available ENM properties (e.g., physicochemical, biological, biokinetics etc.). The read-across prediction is computed as the weighted average of the neighbour ENMs. This novel grouping approach is based on the formulation and the solution of a mixed integer non-linear mathematical programming problem. A specific genetic algorithm scheme was developed to compute an approximate solution, due to the complexity of the problem rendering it practically unsolvable by conventional mathematical algorithms. The second method constructs, a mixed integer-linear optimisation program, which automatically filters out the noisy variables, defines the grouping boundaries based on one of the available properties -which is automatically chosen- and develops specific to each group LASSO linear regression predictive models. The third computational workflow is based on the formulation of a mathematical optimisation methodology that groups the ENMs into regions -according to their endpoint value-, removes the noisy variables, and incorporates the LASSO method for training predictive linear models specific to each region. Finally, k-Nearest Neighbours machine learning methodology was applied for deriving read-across models predicting the cytotoxicity and the biological activity of decorated multiwalled carbon nanotubes using calculated molecular descriptors of their surface ligands, and the zeta-potential of ENMs using geometrical ENMs properties extracted form transmission electron microscopy images. All developed methodologies were applied and validated on benchmark datasets, based on OECD principles, and were compared with methodologies already presented in Literature. They proved to be comparable and, in several cases, outperformed other alternative predictive modelling techniques, illustrating this way their good predictive performance and capabilities. Taking also into account that the grouping, feature selection and model generation steps are fully automated, the proposed methods can be considered as promising new approaches in the field of grouping/read-across modelling.	en
heal.sponsor	The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number: 637)	en
heal.advisorName	Sarimveis, Haralambos
heal.committeeMemberName	Sarimveis, Haralambos
heal.committeeMemberName	Valsami-Jones, Eugenia
heal.committeeMemberName	Charitidis, Constantinos
heal.committeeMemberName	Theodorou, Doros
heal.committeeMemberName	Tsopelas, Fotios
heal.committeeMemberName	Melagraki, Georgia
heal.committeeMemberName	Lynch, Iseult
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Χημικών Μηχανικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	270
heal.fullTextAvailability	false
heal.fullTextAvailability	false