Next generation of informatics tools for big data analytics in drug discovery
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Document Type
Document Version
Director
Mestres i López, Jordi
Tutor
Other authors
Publication Date
Pages
154 p.
Embargo date
Citation
Carrascosa Baena, M. Next generation of informatics tools for big data analytics in drug discovery. Universitat Pompeu Fabra; 2018. handle: http://hdl.handle.net/10803/586011
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Citation
Doctoral program
Programa de doctorat en Biomedicina
Abstract
El paradigma clàssic on un medicament interacciona amb un únic target biològic vinculat a una malaltia es posa en dubte. Actualment es reconeix que un medicament interacciona amb múltiples targets biològics i que aquests targets estan involucrats en multitud de pathways i que s’expressen en una varietat d’òrgans. Amb el creixent reconeixement d’aquesta complexitat, la estratègia reduccionista del procés de descoberta de nous medicaments ha evolucionat cap a estratègies sistèmiques multinivell. Gràcies als avenços tecnològics, hi ha hagut un gran increment de les dades generades en les diverses àrees rellevants en la descoberta de nous medicaments: química, farmacologia, toxicologia, genòmica, metabolòmica, etc fet que ha expandit considerablement la nostra habilitat per general models computacionals amb un rendiment i cobertura creixents. Però darrerament, extreure coneixement d’aquest complex, vast i heterogeni volum de dades no és simple. El principal objectiu d’aquesta tesi es desenvolupar noves eines analítiques i de visualització i investigar la seva capacitat per extreure nou coneixement de dades altament interconnectades; eines integrades a una plataforma flexible que per obtenir respostes simples a preguntes complexes. En particular, farem èmfasi en la navegació per les relacions entre les entitats del sistema (molècules petites i els seus metabòlits, proteïnes com a targets biològics, termes de safety).
The classical silver bullet paradigm of one drug interacting with a single target linked to a disease is currently challenged. It is now widely recognized that one drug interacts with multiple targets and these targets are involved in many biological pathways and expressed in a variety of organs. As the notion of complexity has been gradually accepted, the reductionist drug discovery approach has naturally evolved towards systems multilevel strategies. Thanks to technological advances, there has been a huge increase of data generated in the various fields relevant to drug discovery, namely, chemistry, pharmacology, toxicology, genomics, metabolomics, etc., which has expanded dramatically our ability to generate computational models with increasing performance and coverage. But ultimately, extracting knowledge from this complex, vast and heterogeneous amount of data is not straightforward. The main objective of this Thesis is to develop new interactive analytics and visualization tools and investigate their ability to extract knowledge from highly interconnected data when implemented into an integrated flexible platform to facilitate drawing simple answers from complex questions. In particular, special emphasis will be put in the navigation aspects of the relationships between systemic entities (small molecules and their metabolite, protein targets, safety terms).
The classical silver bullet paradigm of one drug interacting with a single target linked to a disease is currently challenged. It is now widely recognized that one drug interacts with multiple targets and these targets are involved in many biological pathways and expressed in a variety of organs. As the notion of complexity has been gradually accepted, the reductionist drug discovery approach has naturally evolved towards systems multilevel strategies. Thanks to technological advances, there has been a huge increase of data generated in the various fields relevant to drug discovery, namely, chemistry, pharmacology, toxicology, genomics, metabolomics, etc., which has expanded dramatically our ability to generate computational models with increasing performance and coverage. But ultimately, extracting knowledge from this complex, vast and heterogeneous amount of data is not straightforward. The main objective of this Thesis is to develop new interactive analytics and visualization tools and investigate their ability to extract knowledge from highly interconnected data when implemented into an integrated flexible platform to facilitate drawing simple answers from complex questions. In particular, special emphasis will be put in the navigation aspects of the relationships between systemic entities (small molecules and their metabolite, protein targets, safety terms).
Keywords
Data visualization, Data analytics, Safety prediction, Drug discovery, Pharmacology predictions, Visualització de dates, Anàlisi de dades, Predicció de toxicitat, Descoberta de fàrmacs, Prediccions de farmacologia
Subjects
615 - Pharmacology. Therapeutics. Toxicology. Radiology
Publisher
Universitat Pompeu Fabra
