The computer storage, retrieval and searching of generic structures in chemical patents : the machine-readable representation of generic structures.

The nature of the generic chemical structures found in patents is described, with a discussion of the types of statement commonly found in them. The available representations for such structures are reviewed, with particular note being given to the suitability of the representation for searching files of such structures. Requirements for the unambiguous representation of generic structures in an "ideal" storage and retrieval system are discussed. The basic principles of the theory of formal languages are reviewed, with particular consideration being given to parsing methods for context-free languages. The Grammar and parsing of computer programming languages, as an example of artificial formal languages, is discussed. Applications of formal language theory to chemistry and information work are briefly reviewed. GENSAL, a formal language for the unambiguous description of generic structures from patents, is presented. It is designed to be intelligible to a chemist or patent agent, yet sufficiently ABSTRACT formaLised to be amenabLe to computer anaLysis. DetaiLed description is given of the facilities it provides for generic structure representation, and there is discussion of its Limitations and the principLes behind its design. A connection-tabLe-based internaL representation for generic structures, caLLed an ECTR <Extended Connection TabLe Representation) is presented. It is designed to represent generic structures unambiguousLy, and to be generated automatically from structures encoded in GENSAL. It is compared to other proposed representations, and its implementation using data types of the programming Language PascaL described. An interpreter program which generates an ECTR from structures encoded in a subset of the GENSAL Language is presented. The principles of its operation are described. Possible applications of GENSAL outside the area of patent documentation are discussed, and suggestions made for further work on the development of a generic structure storage and retrieval system based on GENSAL and ECTRs

Computational approaches to identify small-molecule inhibitors of non-traditional drug targets

Non-traditional targets for therapeutic intervention are those proteins that have not evolved to bind small molecules, but have instead evolved to bind other macromolecules. Such targets include protein–protein interaction sites, protein–RNA interaction sites and protein–DNA interaction sites. Modulating these biologically important targets will allow us as a community to develop novel therapeutics, but still remains a major challenge. In this thesis, I describe two different computational approaches that I have developed: one for identifying small-molecule inhibitors of protein–protein interactions, and the other for identifying small-molecule inhibitors of protein–RNA interactions. To specifically target protein interaction sites, I have developed a docking method called DARC (Docking Approach using Ray-Casting). This method quantitatively measures the complementarity between the protein surface and a ligand, by using ray-casting to map and compare their shapes. I have applied DARC to carry out a virtual screen against the protein interaction site of the protein Mcl 1, allowing us to identify 6 new inhibitors of this exciting target. To specifically target protein-RNA interactions, I have developed a mimicry-inspired strategy that extracts a “hotspot pharmacophore” from the structure of a protein-RNA complex, and then uses this as a template for ligand-based virtual screening. I have applied this strategy to screen for compounds that inhibit the Musashi-1 / NUMB mRNA interaction, allowing us to identify a new class of compounds that inhibit this interaction in both biochemical and cell-based assays. This thesis is outlined as follows. In the first chapter, I will compare the structural features of inhibitor-bound complexes of traditional versus non-traditional protein targets. In the second chapter, I will present the DARC method and its application to Mcl-1. In the third chapter, I will present various enhancements to DARC method that result in both speed and performance improvements. Finally, in the Fourth chapter I will present the “hotspot mimicry” approach for targeting protein-RNA interactions and application of this approach in identification of inhibitors for Musashi 1 / NUMB mRNA interaction

The purposeful frame- architecture, sociality and space formation

ARCHITECTURE AND SOCIETY This critical practice PhD dissertation, by focusing on my work through the prism of the Practice Research Symposium process, frames the twin concepts of architecture and society and examines their relationship to each other. It particularly focuses on the architect&amp;rsquo;s role in society, as an active agent in space formation, what opportunities that role might offer, and identifies activities and ideas where the architect may make a contribution to a bridging of the twin concepts. With that in mind and firmly noted as the ground of this PhD, it explores my work and ideas and aims to show where these contribute to a greater understanding and wider appreciation of the social engagement possibilities inherent in the profession of architect. The PhD introduces several concepts that constellate the two powerful human ideas of Architecture and Soci- ety and places them within, what I have called The Purposeful Frame. The concepts introduced include three main ideas:- Significant Movement, Social Attitude and Social Potential, with other spin off thoughts, and these will be explored more fully later in this document, through an examination of my work and ideas, and those of other people. MAIN CONCEPTS Encapsulated thoughts and ideas or a short summary on The Purposeful Frame.
Significant Movement &amp;ndash; Distilling and framing the essential movement within a project.
Social Attitude &amp;ndash; Ways of being, embedded within the project. Firstly, opportunities for representing certain social formations, and secondly, possibilities for communality within the programme.
Social Potential &amp;ndash; Valency, a conceptual attempt at calibration of social potential; and tactics and tectonics to improve potential for social occasion; fit, right size, thresholds, mood, etc

A review of molecular representation in the age of machine learning

Funder: UCB; Id: http://dx.doi.org/10.13039/100011110Abstract: Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine‐readable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature‐based, and computer‐learned representations. Three of the most significant representations are simplified molecular‐input line‐entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatic

Prototype of a Conversational Assistant for Satellite Mission Operations

The very first artificial satellite, Sputnik, was launched in 1957 marking a new era. Concurrently, satellite mission operations emerged. These start at launch and finish at the end of mission, when the spacecraft is decommissioned. Running a satellite mission requires the monitoring and control of telemetry data, to verify and maintain satellite health, reconfigure and command the spacecraft, detect, identify and resolve anomalies and perform launch and early orbit operations. The very first chatbot, ELIZA was created in 1966, and also marked a new era of Artificial Intelligence Systems. Said systems answer users’ questions in the most diverse domains, interpreting the human language input and responding in the same manner. Nowadays, these systems are everywhere, and the list of possible applications seems endless. The goal of the present master’s dissertation is to develop a prototype of a chatbot for mission operations. For this purpose implementing a Natural Language Processing (NLP) model for satellite missions allied to a dialogue flow model. The performance of the conversational assistant is evaluated with its implementation on a mission operated by the European Space Agency (ESA), implying the generation of the spacecraft’s Database Knowledge Graph (KG). Throughout the years, many tools have been developed and added to the systems used to monitor and control spacecrafts helping Flight Control Teams (FCT) either by maintaining a comprehensive overview of the spacecraft’s status and health, speeding up failure investigation, or allowing to easily correlate time series of telemetry data. However, despite all the advances made which facilitate the daily tasks, the teams still need to navigate through thousands of parameters and events spanning years of data, using purposely built user interfaces and relying on filters and time series plots. The solution presented in this dissertation and proposed by VisionSpace Technologies focuses on improving operational efficiency whilst dealing with the mission’s complex and extensive databases.O primeiro satélite artificial, Sputnik, foi lançado em 1957 e marcou o início de uma nova era. Simultaneamente, surgiram as operações de missão de satélites. Estas iniciam com o lançamento e terminam com desmantelamento do veículo espacial, que marca o fim da missão. A operação de satélites exige o acompanhamento e controlo de dados de telemetria, com o intuito de verificar e manter a saúde do satélite, reconfigurar e comandar o veículo, detetar, identificar e resolver anomalias e realizar o lançamento e as operações iniciais do satélite. Em 1966, o primeiro Chatbot foi criado, ELIZA, e também marcou uma nova era, de sistemas dotados de Inteligência Artificial. Tais sistemas respondem a perguntas nos mais diversos domínios, para tal interpretando linguagem humana e repondendo de forma similar. Hoje em dia, é muito comum encontrar estes sistemas e a lista de aplicações possíveis parece infindável. O objetivo da presente dissertação de mestrado consiste em desenvolver o protótipo de um Chatbot para operação de satélites. Para este proposito, criando um modelo de Processamento de Linguagem Natural (NLP) aplicado a missoões de satélites aliado a um modelo de fluxo de diálogo. O desempenho do assistente conversacional será avaliado com a sua implementação numa missão operada pela Agência Espacial Europeia (ESA), o que implica a elaboração do grafico de conhecimentos associado à base de dados da missão. Ao longo dos anos, várias ferramentas foram desenvolvidas e adicionadas aos sistemas que acompanham e controlam veículos espaciais, que colaboram com as equipas de controlo de missão, mantendo uma visão abrangente sobre a condição do satélite, acelerando a investigação de falhas, ou permitindo correlacionar séries temporais de dados de telemetria. No entanto, apesar de todos os progressos que facilitam as tarefas diárias, as equipas ainda necessitam de navegar por milhares de parametros e eventos que abrangem vários anos de recolha de dados, usando interfaces para esse fim e dependendo da utilização de filtros e gráficos de series temporais. A solução apresentada nesta dissertação e proposta pela VisionSpace Technologies tem como foco melhorar a eficiência operacional lidando simultaneamente com as suas complexas e extensas bases de dados