Flexible molecular alignment: an industrial case study on quantum algorithmic techniques

Dissertação de mestrado em Engenharia FísicaFlexible molecular alignment is a complex and challenging problem in the area of Medic inal Chemistry. The current approach to this problem does not test all possible alignments, but makes a previous analysis of all the variables and chooses the ones with potentially greater impact in the posterior alignment. This procedure can lead to wrong ”best align ments” since not every data is considered. Quantum computation, due to its natural parallelism, may improve algorithmic solutions for this kind of problems because it may test and/or simulate all possible solutions in an execution cycle. As a case study proposed by BIAL and in collaboration with IBM, the main goal of this dissertation was to study and create quantum algorithms able to refactor the problem of molecular alignment in the new setting of quantum computation. Additionally, the comparison between both classical and quantum solutions was defined as a subsequent goal. During this dissertation and due to its complexity, in order to produce a practical solu tion to this problem, we resorted to a manageable number of conformations per molecule, revisited the classical solution and elaborated a corresponding quantum algorithm. Such algorithm was then tested in both a quantum simulator and a real device. Despite the privileged collaboration with IBM, the quantum simulations were not pro duced in viable time, making them impractical for industry applications. Nonetheless, tak ing in consideration the current point of development of quantum hardware, the suggested solutions still has potential for the future.O alinhamento de moléculas flexíveis é um problema complexo na área de Química Medicinal, onde, mesmo hoje em dia, é um desafio encontrar uma solução. A atual abordagem para este problema não testa todos os possíveis alinhamentos. Em vez disso, realiza uma análise prévia de todas as variáveis e escolhe aquelas com maior potencial de impacto no posterior alinhamento. Este procedimento pode levar a falsos “melhores alinhamentos” visto que nem todos os dados são considerados. A computação quântica, devido ao seu natural paralelismo, pode melhorar as soluções algorítmicas deste tipo de problemas visto que poderá testar e/ou simular todas as possíveis soluções num ciclo de execução. Partindo de um caso de estudo proposto pela BIAL, e em colaboração com a IBM, o objetivo principal desta dissertação foi estudar e criar algoritmos quânticos capazes reformular no contexto de computação quântica o problema de alinhamento de moléculas. Adicionalmente, e como objetivo subsequente, foi prevista a comparação entre os algoritmos clássicos e quânticos. Durante esta dissertação e devido à sua complexidade, de modo a produzir uma solução prática para este problema, foi utilizado um número tratável de conformações por molécula, revisitada a solução clássica e desenvolvido um algoritmo quântico correspondente. Tal algoritmo foi depois testado tanto num simulador quântico como num dispositivo real. Apesar da colaboração privilegiada com a IBM, as simulações quânticas não foram produzidas em tempo viável, tornando-as impraticáveis para aplicações industriais. Não obstante, tendo em consideração o ponto atual de desenvolvimento dos dispositivos quânticos, as soluções propostas terão potencial para o futuro

The topology of fullerenes

Fullerenes are carbon molecules that form polyhedral cages. Their bond structures are exactly the planar cubic graphs that have only pentagon and hexagon faces. Strikingly, a number of chemical properties of a fullerene can be derived from its graph structure. A rich mathematics of cubic planar graphs and fullerene graphs has grown since they were studied by Goldberg, Coxeter, and others in the early 20th century, and many mathematical properties of fullerenes have found simple and beautiful solutions. Yet many interesting chemical and mathematical problems in the field remain open. In this paper, we present a general overview of recent topological and graph theoretical developments in fullerene research over the past two decades, describing both solved and open problems. WIREs Comput Mol Sci 2015, 5:96–145. doi: 10.1002/wcms.1207 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website

Development of New Computational Tools for Analyzing Hi-C Data and Predicting Three-Dimensional Genome Organization

Background: The development of Hi-C (and related methods) has allowed for unprecedented sequence-level investigations into the structure-function relationship of the genome. There has been extensive effort in developing new tools to analyze this data in order to better understand the relationship between 3D genomic structure and function. While useful, the existing tools are far from maturity and (in some cases) lack the generalizability that would be required for application in a diverse set of organisms. This is problematic since the research community has proposed many cross-species "hallmarks" of 3D genome organization without confirming their existence in a variety of organisms. Research Objective: Develop new, generalizable computational tools for Hi-C analysis and 3D genome prediction. Results: Three new computational tools were developed for Hi-C analysis or 3D genome prediction: GrapHi-C (visualization), GeneRHi-C (3D prediction) and StoHi-C (3D prediction). Each tool has the potential to be used for 3D genome analysis in both model and non-model organisms since the underlying algorithms do not rely on any organism-specific constraints. A brief description of each tool follows. GrapHi-C is a graph-based visualization of Hi-C data. Unlike existing visualization methods, GrapHi-C allows for a more intuitive structural visualization of the underlying data. GeneRHi-C and StoHi-C are tools that can be used to predict 3D genome organizations from Hi-C data (the 3D-genome reconstruction problem). GeneRHi-C uses a combination of mixed integer programming and network layout algorithms to generate 3D coordinates from a ploidy-dependent subset of the Hi-C data. Alternatively, StoHi-C uses t-stochastic neighbour embedding with the complete set of Hi-C data to generate 3D coordinates of the genome. Each tool was applied to multiple, independent existing Hi-C datasets from fission yeast to demonstrate their utility. This is the first time 3D genome prediction has been successfully applied to these datasets. Overall, the tools developed here more clearly recapitulated documented features of fission yeast genomic organization when compared to existing techniques. Future work will focus on extending and applying these tools to analyze Hi-C datasets from other organisms. Additional Information: This thesis contains a collection of papers pertaining to the development of new tools for analyzing Hi-C data and predicting 3D genome organization. Each paper's publication status (as of January 2020) has been provided at the beginning of the corresponding chapter. For published papers, reprint permission was obtained and is available in the appendix

Doctor of Philosophy

dissertationServing as a record of what happened during a scientific process, often computational, provenance has become an important piece of computing. The importance of archiving not only data and results but also the lineage of these entities has led to a variety of systems that capture provenance as well as models and schemas for this information. Despite significant work focused on obtaining and modeling provenance, there has been little work on managing and using this information. Using the provenance from past work, it is possible to mine common computational structure or determine differences between executions. Such information can be used to suggest possible completions for partial workflows, summarize a set of approaches, or extend past work in new directions. These applications require infrastructure to support efficient queries and accessible reuse. In order to support knowledge discovery and reuse from provenance information, the management of those data is important. One component of provenance is the specification of the computations; workflows provide structured abstractions of code and are commonly used for complex tasks. Using change-based provenance, it is possible to store large numbers of similar workflows compactly. This storage also allows efficient computation of differences between specifications. However, querying for specific structure across a large collection of workflows is difficult because comparing graphs depends on computing subgraph isomorphism which is NP-Complete. Graph indexing methods identify features that help distinguish graphs of a collection to filter results for a subgraph containment query and reduce the number of subgraph isomorphism computations. For provenance, this work extends these methods to work for more exploratory queries and collections with significant overlap. However, comparing workflow or provenance graphs may not require exact equality; a match between two graphs may allow paired nodes to be similar yet not equivalent. This work presents techniques to better correlate graphs to help summarize collections. Using this infrastructure, provenance can be reused so that users can learn from their own and others' history. Just as textual search has been augmented with suggested completions based on past or common queries, provenance can be used to suggest how computations can be completed or which steps might connect to a given subworkflow. In addition, provenance can help further science by accelerating publication and reuse. By incorporating provenance into publications, authors can more easily integrate their results, and readers can more easily verify and repeat results. However, reusing past computations requires maintaining stronger associations with any input data and underlying code as well as providing paths for migrating old work to new hardware or algorithms. This work presents a framework for maintaining data and code as well as supporting upgrades for workflow computations

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum