Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.

Cette thèse transdisciplinaire réalisée en collaboration avec le laboratoire SPCTS (Sciences des Procédés Céramiques et Traitement de Surface) et l’EA 3842 (Homéostasie cellulaire et pathologies) de l’université de Limoges est un projet de recherche à l’interface entre la biologie et la chimie et a été consacrée à l’étude de l’influence des propriétés physico-chimiques de biocéramiques de phosphate de calcium sur leur comportement biologique in vitro.L’exploration des processus d’interaction entre matériaux et cellules reste une problématique scientifique de premier plan tant d’un point de vue fondamental qu’appliqué pour la mise au point de biomatériaux performants. L’objectif final est d’optimiser l’efficacité thérapeutique des céramiques phosphocalciques comme matériaux de substitution pour la régénération osseuse. La première partie de la thèse est une revue bibliographique générale présentant la problématique actuelle abordée en lien avec les besoins cliniques et les limitations des études actuelles. Les connaissances sur la biologie du tissu osseux sain ainsi que les aspects de régulation du processus de remodelage osseux ont également été abordés dans ce chapitre. Ce chapitre se termine par une synthèse bibliographique sur les biomatériaux et la régénération osseuse. Le chapitre 2 est relatif à la synthèse puis à la caractérisation physico-chimique des matériaux céramiques. Des céramiques de trois compositions chimiques : HA (hydroxyapatite : Ca10(PO4)6(OH)2 , SiHA (hydroxyapatite silicatée : Ca10(PO4)5,6(SiO4)0,42(OH)1,6 et CHA (hydroxyapatite carbonatée : Ca9,5(PO4)5,5(CO3)0,48(OH)1,08(CO3)0,23 , chacune avec deux microstructures différentes : dense ou poreuse, ont été élaborées et rigoureusement caractérisées (porosité, topographie de surface, mouillabilité, potentiel zêta, taille des grains, distribution et taille des pores, surface spécifique). Le chapitre 3 décrit l’approche expérimentale employée pour l’évaluation biologique des interactions matériaux/cellules explorées dans ce travail. Les analyses biologiques ont été réalisées avec deux lignées cellulaires différentes. La lignée cellulaire pré-ostéoblastique MC3T3-E1 et la lignée cellulaire de monocytes/macrophages, précurseurs des ostéoclastes RAW 264.7, (très importantes pour les aspects osseux, mais moins souvent explorées que les lignées ostéoblastiques dans la littérature). Enfin, le chapitre 4 reporte et commente les résultats biologiques obtenus dans ce travail. Tous les biomatériaux évalués dans cette étude sont biocompatibles, néanmoins, le biomatériau poreux CHA s’est avéré le plus prometteur des six variantes de biomatériaux testés. / This transdisciplinary thesis, carried out in collaboration with the SPCTS laboratory (sciences of ceramic processes and surface treatment) and EA 3842 (Cellular homoeostasis and pathologies) of the University of Limoges, is a research project at the interface between biology and chemistry and was devoted to the study of the influence of the physico-chemical properties of calcium phosphate bioceramics on their biological behavior in vitro.The exploration of the processes of interaction between materials and cells remains a major scientific issue, both from a fundamental and applied point of view for the development of highperformance biomaterials. The ultimate objective is to optimize the therapeutic efficiency of phosphocalcic ceramics as substitute materials for bone regeneration.The first part of the thesis is a general bibliographic review presenting the current issues tackled with the clinical needs and limitations of current studies. Knowledge of the biology of healthy bone tissue as well as the regulatory aspects of the bone remodeling process was also discussed in this chapter. It includes also a bibliographic overview of biomaterials and bone regeneration.Chapter 2 relates to the synthesis and the physico-chemical characterization of ceramic materials. HA (hydroxyapatite: Ca10 (PO4) 6 (OH) 2, SiHA (silicated hydroxyapatite: Ca10 (PO4) 5.6 (SiO4) 0.42 (OH) 1.6 and CHA (carbonated hydroxyapatite: Ca9.5 (PO4) 5.5 (CO3) 0.48 (OH) 1.08 (CO3) 0.23, ceramics each with two different microstructures : dense or porous, have been elaborated and thoroughly characterized (porosity, surface topography, wettability, zeta potential, grain size, pore size and distribution, specific surface area). Chapter 3 describes the experimental approach used for the biological evaluation of the interactions between materials and cells. Biological analyzes were performed with two different cell lines. The pre-osteoblastic MC3T3-E1 cell line and the RAW 264.7cell line of monocytes / macrophages, precursors of the steoclasts, (very important for the bone aspects, but less often explored than the osteoblastic lines in the literature). Finally, Chapter 4 reports and comments on the biological results obtained in this work. All biomaterials evaluated are biocompatible, nevertheless, the porous CHA biomaterial was the most promising of the six variants of biomaterials tested.

Fungus Gloeophyllum trabeum (Agaricomytes: Gloeophyllates) is a brown rot wood-decay fungus which produces a vast spectrum of volatile secondary metabolites. Scientific publications state, that one of the metabolites produced by G. trabeum, can be the substance (3Z,6Z,8E)-dodecatrien-1-ol. This chemical substance is also the main component of trail-following pheromone of Rhinotermitidae termite family. In this diploma thesis, I was trying to verify whether various species of G. trabeum are in fact capable of producing the substance (3Z,6Z,8E)-dodecatrien-1-ol. I was also focusing on the effects of saccharides, present in nutrient solutions, on quantitative and qualitative change in composition of volatile secondary metabolites produced by G. trabeum. The saccharides I used for my research were - maltose, fructose, sucrose, xylose, and mannose. The analysis was made by using comprehensive two-dimensional gas chromatography separation technique with time-of-flight mass spectrometric detection (GC×GC-TOFMS). During my research I discovered that one of obtained species of G. trabeum can produce substance (3Z,6Z,8E)-dodecatrien-1-ol, but only under specific conditions. It is produced when cultivating on Petri dishes on agar - cellulose growth media. The measurement was further validated by...

Orientador: Andréa Aparecida Zacharias / Resumo: Na atualidade, uma série de desastres inter-relacionados ganharam notoriedade no Brasil e no mundo, reunindo episódios que marcaram crescentes perdas, humanas e econômicas, associadas aos riscos e suas consequências. O processo de urbanização, juntamente com a impermeabilização do solo, retificação e assentamento em cursos d’água e encostas, contribuíram para o aumento do impacto de inundações, enchentes e vários outros processos advindos da ação antrópica que levam ao risco socioambiental. Somam-se nas últimas cinco décadas mais de dez mil mortes em desastres naturais no Brasil, a maioria destes relacionadas a inundações e queda de encostas. A magnitude de um desastre está vinculada com os fenômenos sociais, econômicos e demográficos, entre outros, e contribuem para aumentar a vulnerabilidade e exposição da população. O recorte espacial aqui analisado compreende a Região Administrativa do Lavapés, macrozona que envolve a área urbana do município de Bragança Paulista/SP. Bragança Paulista sofre, historicamente, uma série de problemas socioeconômicos e ambientais. Destaca-se o aumento na magnitude e frequência das enchentes devido à extensa cobertura impermeabilizada, pois grande parte da água que antes era infiltrada no solo, passa então a compor o volume que escoa superficialmente. O objetivo principal desta pesquisa funda-se sobre o estudo da espacialidade da vulnerabilidade socioambiental aos riscos hidrológicos, em específico as inundações, considerando a atuação dos fato... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: A series of interrelated disasters have currently gained prominence over the Brazil and worldwide, gathering episodes that have resulted in increasing losses, both human and economic, related to risks and their consequences. The urbanization process, along with degree of saturation, soil imperviousness, rectification and improper settlement on hillslopes and near to the rivers, have contributed to an increasing impact of floods and several human-induced processes that lead to socio-environmental risk. In the last five decades, there have been more than ten thousand deaths caused by natural disasters, most of them related to floods and landslide. The magnitude of a disaster is related to social, economic and demographic phenomena, among others, and contributes to increasing the population's vulnerability and exposure. We analyzed the Lavapés Administrative Region, a macrozone encompassing the urban area of Bragança Paulista/SP municipality. The city of Bragança Paulista have suffered, historically, a plenty of socioeconomic and environmental issues. The increasing intensity and frequency of the floods are noteworthy due to extensive impervious cover, since large water volumes that were previously infiltrating, now become part of the surface runoff. The main objective here relies on the spatial distribution of socio-environmental vulnerability related to hydrological risks, particularly floods, considering the triggering factors in urban areas. The methodological procedures are... (Complete abstract click electronic access below) / Mestre

The diploma work was based on the evaluation of dental casts of patients with bilateral cleft lip and palate (BCLP) with a mean age of 10. Patients consist of two groups, patients without defect in speech and with speech impairment (palatolaly). Palatolalies in the literature are primarily associated with velopharyngeal insufficiency. The study tested the working hypothesis that in the failure of speech is involved a different, specific in some way, palatal shape. Dental casts were scanned using a laser scanner and analyzed by 3-D geometric morphometry and multivariate statistics: principal component analysis (PCA), linear regression analysis and finite element analysis (FESA). Using linear regression it was found that the shape of the palate is affected in younger individuals by age, and so had to be 5 patients excluded for further analysis. Patients with palatolaly have lower variability the palatal shape than patients without palatolalie, so their palates are similar to each other and have a specific shape. Palates are wider and lower than in individuals without speech disorder and they have a characteristic deepening behind the anterior part of the palate. We assume that these features in palate morphology primarily the lower arch and the substantial deepening are most likely to affect the...

Corruption is a social evil that resonates far and deep in societies, eroding trust in governance, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. It is a multidimensional and complex societal malady that occurs in various forms and contexts. As such, any effort to combat corruption must be accompanied by a thorough examination of the attributes that might play a key role in exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that influence corruption, using machine learning techniques, neural network analysis, and time series causal relationship analysis and aggregated data from 113 countries from 2007 to 2017. The results suggest that improvements in technological readiness, human development index, and e-governance index have the most profound impacts on corruption reduction. This dissertation discusses corruption at each phase of infrastructure systems development and engineering ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical efficiency measurement methods to measure infrastructure inefficiencies, and to rank infrastructure administrative jurisdictions at the state level. An efficiency frontier is developed using optimization and the highest performing jurisdictions are identified. The dissertation’s framework could serve as a starting point for governmental and non-governmental oversight agencies to study forms and contexts of corruption and inefficiencies, and to propose influential methods for reducing the instances. Moreover, the framework can help oversight agencies to promote the overall accountability of infrastructure agencies by establishing a clearer connection between infrastructure investment and performance, and by carrying out comparative assessments of infrastructure performance across the jurisdictions under their oversight or supervision.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Computer based assessments have become one of the largest growing sectors in both nonacademic and academic establishments. Successful computer based assessments require security against impersonation and fraud and many researchers have proposed the use of Biometric technologies to overcome this issue. Biometric technologies are defined as a computerised method of authenticating an individual (character) based on behavioural and physiological characteristic features. Basic biometric based computer based assessment systems are prone to security threats in the form of fraud and impersonations. In a bid to combat these security problems, keystroke dynamic technique and facial biometric recognition was introduced into the computer based assessment biometric system so as to enhance the authentication ability of the computer based assessment system. The keystroke dynamic technique was measured using latency and pressure while the facial biometrics was measured using principal component analysis (PCA). Experimental performance was carried out quantitatively using MATLAB for simulation and Excel application package for data analysis. System performance was measured using the following evaluation schemes: False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) and Accuracy (AC), for a comparison between the biometric computer based assessment system with and without the keystroke and face recognition alongside other biometric computer based assessment techniques proposed in the literature. Successful implementation of the proposed technique would improve computer based assessment’s reliability, efficiency and effectiveness and if deployed into the society would improve authentication and security whilst reducing fraud and impersonation in our society.

This thesis presents an application of Principal Component Analysis (PCA) and Hierarchical PCA to credit spreads. The aim is to identify the underlying factors that drive the behavior of credit spreads as well as the left over idiosyncratic risk, which is crucial for risk management and pricing of credit derivatives. The study employs a dataset from the Swedish market of credit spreads for different maturities and ratings, split into Covered Bonds and Corporate Bonds, and performs PCA to extract the dominant factors that explain the variation in the data of the former set. The results show that most of the systemic movements in Swedish covered bonds can be extracted using a mean which coincides with the first principal component. The report further explores the idiosyncratic risk of the credit spreads to further the knowledge regarding the dynamics of credit spreads and improving risk management in credit portfolios, specifically in regards to new regulation in the form of the Fundemental Review of the Trading Book (FRTB). The thesis also explores a more general model on corporate bonds using HPCA and K-means clustering. Due to data issues it is less explored but there are useful findings, specifically regarding the feasibility of using clustering in combination with HPCA. / I detta arbete presenteras en tillämpning av Principal Komponent Analysis (PCA) och Hierarkisk PCA på kreditspreadar. Syftet är att identifiera de underliggande faktorer som styr kreditspreadarnas beteende samt den kvarvarande idiosynkratiska risken, vilket är avgörande för riskhantering och prissättning av diverse kreditderivat. I studien används en datamängd från den svenska marknaden med kreditspreadar för olika löptider och kreditbetyg, uppdelat på säkerställda obligationer och företagsobligationer, och PCA används för att ta fram de mest signifikanta faktorerna som förklarar variationen i data för de förstnämnda obligationerna. Resultaten visar att de flesta av de systematiska rörelserna i svenska säkerställda obligationer kan extraheras med hjälp av ett medelvärde som sammanfaller med den första principalkomponenten. I rapporten undersöks vidare den idiosynkratiska risken i kreditspreadarna för att öka kunskapen om dynamiken i kreditspreadarna och förbättre riskhanteringen i kreditportföljer, särskilt med tanke på regelverket "Fundemental Review of the Tradring book" (FRTB). I rapporten undersöktes vidare en mer allmän modell för företagsobligationer med hjälp av HPCA och K-means-klustering. På grund av dataproblem är den mindre utforstkad, men det finns användbara resultat, särskild när det gäller möjligheten att använda kluster i kombination med HPCA.

[ES] Esta tesis, desarrollada bajo una beca de formación de personal investigador de la Universitat Politècnica de València, tiene como objetivo proponer y aplicar metodologías de Statistical Machine Learning en contextos de Ingeniería Biomédica. Este concepto pretende aunar el uso de modelos de aprendizaje automático junto con la búsqueda de comprensión e interpretabilidad clásica del razonamiento estadístico, dando lugar a soluciones tecnológicas de problemas biomédicos que no pasen únicamente por el objetivo de optimizar el desempeño predictivo de los modelos. Para ello, se han dibujado dos objetivos principales que vertebran además el documento: proponer metodologías novedosas dentro del paraguas del Statistical Machine Learning, y aplicar soluciones a problemas biomédicos reales manteniendo esta filosofía en mente. Estos objetivos se han materializado en contribuciones metodológicas para la simulación de valores atípicos y la imputación de datos faltantes en presencia de datos atípicos, y en contribuciones aplicadas a casos reales para la mejora de procesos de atención médica, la mejora en el diagnóstico y pronóstico de enfermedades, y la estandarización de procedimientos de medición en entornos biotecnológicos. Dichas contribuciones se han artículado en capítulos correspondientes a las dos partes principales ya mencionadas. Finalmente, las conclusiones y líneas futuras cierran el documento, recalcando los mensajes principales de las contribuciones de la tesis doctoral en general, y sentando además las bases para líneas futuras que se han dibujado a consecuencia del trabajo realizado a lo largo del doctorado. / [CA] Aquesta tesi, desenvolupada sota una beca de formació de personal investigador de la Universitat Politècnica de València, té com a objectiu proposar i aplicar metodologies de Statistical Machine Learning en contextos d'Enginyeria Biomèdica. Aquest concepte pretén unir l'ús de models d'aprenentatge automàtic juntament amb la cerca de comprensió i interpretació clàssica del raonament estadístic, donant lloc a solucions tecnològiques de problemes biomèdics que no passen únicament per l'objectiu d'optimitzar el rendiment predictiu dels models. Per a això, s'han dibuixat dos objectius principals que vertebren a més el document: proposar metodologies noves dins del paraigua del Statistical Machine Learning, i aplicar solucions a problemes biomèdics reals mantenint aquesta filosofia en ment. Aquests objectius s'han materialitzat en contribucions metodològiques per a la simulació de valors atípics i la imputació de dades mancants en presència de valors atípics, i en contribucions aplicades a casos reals per a la millora de processos d'atenció mèdica, la millora en el diagnòstic i pronòstic de malalties, i l'estandardització de procediments de mesurament en entorns biotecnològics. Aquestes contribucions s'han articulat en capítols corresponents a les dues parts principals ja esmentades. Finalment, les conclusions i línies futures tanquen el document, recalant els missatges principals de les contribucions, de la tesi doctoral en general, i assentant a més les bases per a línies futures que s'han dibuixat com a consequència del treball realitzat al llarg del doctorat. / [EN] This thesis, developed under a research personnel formation grant from the Universitat Politècnica de València, aims to propose and apply methodologies of Statistical Machine Learning in Biomedical Engineering contexts. This concept seeks to combine machine learning models with the classic understanding and interpretability of statistical reasoning, resulting in technological solutions for biomedical problems that go beyond solely optimizing the predictive performance of models. To achieve this, two main objectives have been outlined, which also structure the document: proposing novel methodologies within the umbrella of Statistical Machine Learning and applying solutions to real biomedical problems while keeping this philosophy in mind. These objectives have materialized into methodological contributions for simulating outliers and imputing missing data in the presence of outliers and applied contributions to real cases for improving healthcare processes, enhancing disease diagnosis and prognosis, and standardizing measurement procedures in biotechnological environments. These contributions are articulated in chapters corresponding to the aforementioned two main parts. Finally, the conclusions and future lines of research conclude the document, reiterating the main messages of the contributions and the overall doctoral thesis and laying the groundwork for future lines of inquiry stemming from the work conducted throughout the doctorate. / González Cebrián, A. (2024). Statistical Machine Learning in Biomedical Engineering [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203529

En sjukdom kan många gånger härledas till en kaskadereaktion mellan proteiner, co-faktorer och substrat. Denna kaskadreaktion blir många gånger målet för att behandla sjukdomen med läkemedel. För att designa nya läkemedelsmoleyler används vanligen datorbaserade verktyg. Denna design av läkemedelsmolekyler drar stor nytta av att målproteinet är känt och då framförallt dess tredimensionella (3D) struktur. Är 3D-strukturen känd kan man utföra så kallad struktur- och datorbaserad molekyldesign, 3D-geometrin (f.f.a. för inbindningsplatsen) blir en vägledning för designen av en ny molekyl. Många faktorer avgör interaktionen mellan en molekyl och bindningsplatsen, till exempel fysikalisk-kemiska egenskaper hos molekylen och bindningsplatsen, flexibiliteten i molekylen och målproteinet, och det omgivande lösningsmedlet. För att strukturbaserad molekyldesign ska fungera väl måste två viktiga steg utföras: i) 3D anpassning av molekyler till bindningsplatsen i ett målprotein (s.k. dockning) och ii) prediktion av molekylers affinitet för bindningsplatsen. Huvudsyftena med arbetet i denna avhandling var som följer: i) skapa modeler för att prediktera affiniteten mellan en molekyl och bindningsplatsen i ett målprotein; ii) förfina molekyl-protein-geometrin som skapas vid 3D-anpassning mellan en molekyl och bindningsplatsen i ett målprotein (s.k. dockning); iii) karaktärisera proteiner och framför allt deras sekundärstruktur; iv) bedöma effekten av olika matematiska beskrivningar av lösningsmedlet för förfining av 3D molekyl-protein-geometrin skapad vid dockning och prediktion av molekylers affinitet för proteiners bindningsfickor. Ett övergripande syfte var att använda kemometriska metoder för modellering och dataanalys på de ovan nämnda punkterna. För att sammanfatta så presenterar denna avhandling metoder och resultat som är användbara för strukturbaserad molekyldesign. De rapporterade resultaten visar att det är möjligt att skapa kemometriska modeler för prediktion av molekylers affinitet för bindningsplatsen i ett protein och att dessa presterade lika bra som andra vanliga metoder. Dessutom kunde kemometriska modeller skapas för att beskriva effekten av hur inställningarna för olika parametrar i dockningsprogram påverkade den 3D molekyl-protein-geometrin som dockingsprogram skapade. Vidare kunde kemometriska modeller andvändas för att öka förståelsen för deskriptorer som beskrev sekundärstrukturen i proteiner. Förfining av molekyl-protein-geometrin skapad genom dockning gav liknande och ickesignifikanta resultat oberoende av vilken matematisk modell för lösningsmedlet som användes, förutom för ett fåtal (sex av 30) fall. Däremot visade det sig att användandet av en förfinad geometri var värdefullt för prediktion av molekylers affinitet för bindningsplatsen i ett protein. Förbättringen av prediktion av affintitet var markant då en Poisson-Boltzmann beskrivning av lösningsmedlet användes; jämfört med prediktionerna gjorda med ett dockningsprogram förbättrades korrelationen mellan beräknad affintiet och uppmätt affinitet med 0,7 (R2). / A disease is often associated with a cascade reaction pathway involving proteins, co-factors and substrates. Hence to treat the disease, elements of this pathway are often targeted using a therapeutic agent, a drug. Designing new drug molecules for use as therapeutic agents involves the application of methods collectively known as computer-aided molecular design, CAMD. When the three dimensional (3D) geometry of a macromolecular target (usually a protein) is known, structure-based CAMD is undertaken and structural information of the target guides the design of new molecules and their interactions with the binding sites in targeted proteins. Many factors influence the interactions between the designed molecules and the binding sites of the target proteins, such as the physico-chemical properties of the molecule and the binding site, the flexibility of the protein and the ligand, and the surrounding solvent. In order for structure-based CAMD to be successful, two important aspects must be considered that take the abovementioned factors into account. These are; i) 3D fitting of molecules to the binding site of the target protein (like fitting pieces of a jigsaw puzzle), and ii) predicting the affinity of molecules to the protein binding site. The main objectives of the work underlying this thesis were: to create models for predicting the affinity between a molecule and a protein binding site; to refine the geometry of the molecule-protein complex derived by or in 3D fitting (also known as docking); to characterize the proteins and their secondary structure; and to evaluate the effects of different generalized-Born (GB) and Poisson-Boltzmann (PB) implicit solvent models on the refinement of the molecule-protein complex geometry created in the docking and the prediction of the molecule-to-protein binding site affinity. A further objective was to apply chemometric methodologies for modeling and data analysis to all of the above. To summarize, this thesis presents methodologies and results applicable to structure-based CAMD. Results show that predictive chemometric models for molecule-to-protein binding site affinity could be created that yield comparable results to similar, commonly used methods. In addition, chemometric models could be created to model the effects of software settings on the molecule-protein complex geometry using software for molecule-to-binding site docking. Furthermore, the use of chemometric models provided a more profound understanding of protein secondary structure descriptors. Refining the geometry of molecule-protein complexes created through molecule-to-binding site docking gave similar results for all investigated implicit solvent models, but the geometry was significantly improved in only a few examined cases (six of 30). However, using the geometry-refined molecule-protein complexes was highly valuable for the prediction of molecule-to-binding site affinity. Indeed, using the PB solvent model it yielded improvements of 0.7 in correlation coefficients (R2) for binding affinity parameters of a set of Factor Xa protein drug molecules, relative to those obtained using the fitting software.