定量的構造物性相関/定量的構造活性相関モデルの逆解析を利用した化学構造創出に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 船津 公人, 東京大学教授 酒井 康行, 東京大学准教授 杉山 弘和, 東京大学准教授 伊藤 大知, 京都大学特任教授 奧野 恭史, スイス連邦工科大学教授 Gisbert SchneiderUniversity of Tokyo(東京大学

Quantitative Structure-Property Relationship Modeling & Computer-Aided Molecular Design: Improvements & Applications

The objective of this work was to develop an integrated capability to design molecules with desired properties. An automated robust genetic algorithm (GA) module has been developed to facilitate the rapid design of new molecules. The generated molecules were scored for the relevant thermophysical properties using non-linear quantitative structure-property relationship (QSPR) models. The descriptor reduction and model development for the QSPR models were implemented using evolutionary algorithms (EA) and artificial neural networks (ANNs). QSPR models for octanol-water partition coefficients (Kow), melting points (MP), normal boiling points (NBP), Gibbs energy of formation, universal quasi-chemical (UNIQUAC) model parameters, and infinite-dilution activity coefficients of cyclohexane and benzene in various organic solvents were developed in this work. To validate the current design methodology, new chemical penetration enhancers (CPEs) for transdermal insulin delivery and new solvents for extractive distillation of the cyclohexane + benzene system were designed. In general, the use of non-linear QSPR models developed in this work provided predictions better than or as good as existing literature models. In particular, the current models for NBP, Gibbs energy of formation, UNIQUAC model parameters, and infinite-dilution activity coefficients have lower errors on external test sets than the literature models. The current models for MP and Kow are comparable with the best models in the literature. The GA-based design framework implemented in this work successfully identified new CPEs for transdermal delivery of insulin, with permeability values comparable to the best CPEs in the literature. Also, new solvents for extractive distillation of cyclohexane/benzene with selectivities two to four times that of the existing solvents were identified. These two case studies validate the ability of the current design framework to identify new molecules with desired target properties.Chemical Engineerin

Índice semi-empírico eletrotopológico: um novo descritor molecular usado em estudos de correlação quantitativa entre estrutura e retenção cromatográfica para compostos de interesse

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas, Programa de Pós-Graduação em Química, Florianópolis, 2009.Neste trabalho, um novo índice topológico é proposto, baseado na observação de que a retenção cromatográfica de alcanos e alcenos depende fortemente da carga atômica líquida dos átomos de carbono dessas moléculas. O índice semi-empírico eletrotopológico ( ) é aplicado para prever a retenção cromatográfica de hidrocarbonetos alifáticos, aldeídos, cetonas e ésteres. A correlação linear simples entre os índices de retenção cromatográficos e os valores dos para alcanos e alcenos é de boa qualidade e estabilidade interna conforme os parâmetros estatísticos: r= 0,9990; s=10,74; N=179. Os modelos de QSRR para aldeídos e cetonas usando o são também de boa qualidade: r = 0,9994; s = 10,31; N = 15 para aldeídos e r = 0,9997; s = 11,72; N = 42 para cetonas. Modelos de QSRR utilizando para ésteres alifáticos em diferentes fases estacionárias apresentam parâmetros estatísticos variando entre: r=0,9990 a 0,9959; e s=7,65 a 16,02. Um modelo combinado para a predição do índice de retenção usando o índice proposto e a polaridade de McReynolds é de boa qualidade estatística conforme os parâmetros de mérito: r = 0,9978; s = 12,94; N = 500. Este índice codifica tanto a distribuição de carga quanto detalhes estruturais, relacionados ao tamanho, ramificação e presença de heteroátomos, possibilitando novos estudos de QSPR para diferentes funções orgânicas

Hierarchical visualization of large networks

We expose the principles of agglomerative clustering of networks and propose a new efficient link clustering algorithm with a relational constraint, bound implicitly to the corresponding line graph of the input network. Along we develop dissimilarity measures, which besides the network structure consider properties of network elements. We evaluate the algorithm on a set of networks, including bibliographic networks from the field of topological indices. Using existent and new scientometric network analysis approaches we analyze them in detail. We design a method for general hierarchy visualization and develop a visualization method for mobile networks. We use the methods on suitable networks. Considering the principles of abstraction and interactivity we develop a new extendable tool for continuous analysis and visualization of large networks – net.Plexor, which introduces new structured real-time approaches into the network analysis, advanced methods of visualization and upper methods. We conclude the work with an overview of network file formats, and give advice on network data collection and storage

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise