Towards quantum-chemical method development for arbitrary basis functions

We present the design of a flexible quantum-chemical method development framework, which supports employing any type of basis function. This design has been implemented in the light-weight program package molsturm, yielding a basis-function-independent self-consistent field scheme. Versatile interfaces, making use of open standards like python, mediate the integration of molsturm with existing third-party packages. In this way both rapid extension of the present set of methods for electronic structure calculations as well as adding new basis function types can be readily achieved. This makes molsturm well-suitable for testing novel approaches for discretising the electronic wave function and allows comparing them to existing methods using the same software stack. This is illustrated by two examples, an implementation of coupled-cluster doubles as well as a gradient-free geometry optimisation, where in both cases, an arbitrary basis functions could be used. molsturm is open-source and can be obtained from https://molsturm.org.Comment: 15 pages and 7 figure

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Size does not matter: a molecular insight into the biological activity of chemical fragments utilizing computational approaches.

Masters Degree. University of KwaZulu-Natal, Durban.Insight into the functional and physiological state of a drug target is of essential importance in the drug discovery process, with the lack of emerging (3D) drug targets we propose the integration of homology modeling which may aid in the accurate yet efficient construction of 3D protein structures. In this study we present the applications of homology modeling in drug discovery, a conclusive route map and detailed technical guideline that can be utilised to obtain the most accurate model. Even with the presence of available drug targets and substantial advancements being made in the field of drug discovery, the prevalence of incurable diseases still remains at an all-time high. In this study we explore the biological activity of chemically derived fragments from natural products utilising a range of computational approaches and implement its use in a new route towards innovative drug discovery. A potential avenue referred to as the reduce to maximum concept recently proposed by organic chemists, entails reducing the size of a chemical compound to obtain a structural analogs with retained or enhanced biological activity, better synthetic approachability and reduced toxicity. Displaying that size may not in fact matter. Molecular dynamic simulations along with toxicity profiling were comparatively performed, on natural compound Anguinomycin D and its derived analog SB 640 each in complex with the CRM1 protein which plays an avid role in cancer pathogenesis. Each system was post-dynamically studied to comprehend structural dynamics adopted by the parent compound to that exhibited by the analog. Although being reduced by 60% the analog SB 640 displayed an overall exhibition of attractive pharmacophore properties which include minimal reduction in binding affinity, enhanced synthetic approachability and reduced toxicity in comparison to the parent compound. Potent inhibitor of CRM1, Leptomycin B (LMB) displayed substantial inhibition of the CRM1 export protein by binding to four of the PKIαNES residues (ϕ0, ϕ1, ϕ2, ϕ3, and ϕ4) present within the hydrophobic binding groove of CRM1. Although being drastically reduced in size and lacking the presence of the polyketide chain present in the parent compound Anguinomycin D and LMB the analog SB 640 displaced three of these essential NES residues. The potential therapeutic activity of the structural analog remains undeniable, however the application of this approach in drug design still remains ambiguous as to which chemical fragments must be retained or truncated to ensure retention or enhanced pharmacophore properties. In this study we aimed to the use of thermodynamic calculations, which was accomplished by incorporating a MM/GBSA per-residue energy contribution footprint from molecular dynamics simulation. The proposed approach was generated for each system. Anguinomycin D and analog SB 640 each in complex with CRM1 protein, each system formed interactions with the conserved active site residues Leu 536, Thr 575, Val 576 and Lys 579. These residues were highlighted as the most energetically favourable amino acid residues contributing substantially to the total binding free energy. Thus implying a conserved selectivity and binding mode adopted by both compounds despite the omission of the prominent polyketide chain in the analog SB 640, present in the parent compound. A strategic computational approach presented in this study could serve as a beneficial tool to enhance novel drug discovery. This entire work provides an invaluable contribution to the understanding of the phenomena underlying the reduction in the size of a chemical compound to obtain the most beneficial pharmacokinetic properties and could largely contribute to the design of potent analog inhibitors for a range of drug targets implicated in the orchestration of diseases

Computational aerodynamics and artificial intelligence

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics

Unweaving complex reactivity: graph-based tools to handle chemical reaction networks

La informació a nivell molecular obtinguda mitjançant estudis "in silico" s’ha establert com una eina essencial per a la caracterització de mecanismes de reacció complexos. A més, l’aplicabilitat de la química computacional s’ha vist substancialment ampliada a causa de l’increment continuat de la potència de càlcul disponible durant les darreres dècades. Així, no només han augmentat la precisió dels mètodes a utilitzar o la mida dels sistemes a modelitzar sinó també el grau de detall que es pot aconseguir en les descripcions mecanístiques resultants. Tanmateix, aquestes caracteritzacions més profundes, usualment assistides per tècniques d’automatització que permeten l’exploració de regions més extenses de l’espai químic, suposen un increment de la complexitat dels sistemes estudiats i per tant una limitació de la seva interpretabilitat. En aquesta Tesi s’han proposat, desenvolupat i posat a prova diverses eines amb el fi de fer el processament d’aquest tipus de xarxes de reacció químiques (CRNs) més simple i millorar la comprensió de processos reactius i catalítics complexos. Aquesta col·lecció d’eines té com fonament la utilització de grafs per modelitzar les xarxes (CRNs) corresponents, per poder fer servir els mètodes de la Teoria de Grafs (cerca de camins, isomorfismes...) en un context químic. Més concretament, aquestes eines inclouen amk-tools, una llibreria per a la visualització interactiva de xarxes de reacció descobertes de manera automàtica, gTOFfee, per a l’aplicació del "energy span model" pel càlcul de la freqüència de recanvi de cicles catalítics complexos calculats computacionalment, i OntoRXN, una ontologia per descriure CRNs de forma semàntica, integrant la topologia de la xarxa i la informació calculada en una única entitat organitzada segons els principis del "Semantic Data".La información a nivel molecular obtenida por medio de estudios "in silico" se ha convertido en una herramienta indispensable para la caracterización y comprensión de mecanismos de reacción complejos. Asimismo, la aplicabilidad de la química computacional se ha ampliado sustancialmente como consecuencia del continuo incremento de la potencia de cálculo durante las últimas décadas. Así, no sólo han aumentado la precisión de los métodos o el tamaño de los sistemas modelizables, sino también el grado de detalle en la descripción mecanística. Sin embargo, aumentar la profundidad de la caracterización de un sistema químico, usualmente a través de técnicas de automatización que permiten explorar ecciones más extensas del espacio químico, supone un aumento en la complejidad de los sistemas resultantes, dificultando la interpretación de los resultados. En esta Tesis se han propuesto, desarrollado y puesto a prueba distintas herramientas para simplificar el procesado de este tipo de redes de reacción químicas (CRNs), con el fin de mejorar la comprensión de procesos reactivos y catalíticos complejos. Este conjunto de herramientas se basa en el uso de grafos para modelizar las redes (CRNs) correspondientes, con tal de poder emplear los métodos de la Teoría de Grafos (búsqueda de caminos, isomorfismos...) bajo un contexto químico. Concretamente, estas herramientas incluyen amk-tools, para la visualización interactiva de redes de reacción descubiertas automáticamente, gTOFfee, para la aplicación del “energy span model” para calcular la frecuencia de recambio de ciclos catalíticos complejos caracterizados computacionalmente, y OntoRXN, una ontología para describir CRNs de manera semántica, integrando la topología de la red y la información calculada en una única entidad organizada bajo los principios del “Semantic Data”.The molecular-level insights gathered through "in silico" studies have become an essential asset for the elucidation and understanding of complex reaction mechanisms. Indeed, the applicability of computational chemistry has strongly widened due to the vast increase in computational power along the last decades. In this sense, not only the accuracy of the applied methods or the size of the target systems have increased, but also the level of detail attained for the mechanistic description. However, performing deeper descriptions of chemical systems, most often resorting to automation techniques that allow to easily explore larger parts of the chemical space, comes at the cost of also augmenting their complexity, rendering the results much harder to interpret. Throughout this Thesis, we have proposed, developed and tested a collection of tools aiming to process this kind of complex chemical reaction networks (CRNs), in order to provide new insights on reactive and catalytic processes. All of these tools employ graphs to model the target CRNs, in order to be able to use the methods of Graph Theory (e.g. path searches, isomorphisms...) in a chemical context. The tools that are discussed include amk-tools, a framework for the interactive visualization of automatically discovered reaction networks, gTOFfee, for the application of the energy span model to compute the turnover frequency of computationally characterized catalytic cycles, and OntoRXN, an ontology for the description of CRNs in a semantic manner integrating network topology and calculation information in a single, highly-structured entity