Fragment-to-Lead Optimization with Automated and Iterative Virtual Screening

dc.contributor
Universitat de Barcelona. Departament de Farmàcia i Tecnologia farmacèutica i Físicoquímica
dc.contributor.author
Rachman, Moira
dc.date.accessioned
2021-05-28T06:47:26Z
dc.date.available
2021-09-23T02:00:12Z
dc.date.issued
2020-09-23
dc.identifier.uri
http://hdl.handle.net/10803/671756
dc.description.abstract
Fragment-based drug design is an established strategy of finding new drugs. Instead of doing mass screening of chemical libraries containing compounds that are already lead-like, starting from a small fragment can be be a more efficient strategy, because fragment space can actually represent a much larger chemical space than an equally sized library of lead-like compounds. This strategy has led to the discovery of binders for targets where high-throughput screening has previously failed. However, because fragments are small they bind with low affinity, and must be optimized into high affinity and lead-like compounds, and fragment to lead optimization is challenging. This thesis describes the development and validation of an automated fragment-to-lead optimization pipeline. The pipeline can be seen as a focused virtual screening of the chemical space surrounding a given fragment. The pipeline does the virtual screening iteratively to harnesses information about the chemotypes and features of the hits. It does this by determining the most likely to bind analogues through complementary structure-based methods, and then using the best hits to determine the chemical space for the next round of virtual screening. It has been developed to be scalable to screen the continuously growing available libraries. Furthermore, it performs structure-based scaffold hopping, to explore as much chemical space as possible. It's iterative nature makes it possible to seamlessly integrate it into drug discovery pipelines, and also makes it possible to control the ligand efficiency, i.e. maintaining only the most important part of the molecules, necessary for molecular recognition. Our results show that the platform is capable of finding active ligands for known targets such as BRD4, HSP90, DYRK1A, but also unknown targets e.g. NUDT21. Furthermore, it's been shown to be at least as equally successful as other virtual screening methods.
en_US
dc.format.extent
197 p.
en_US
dc.format.mimetype
application/pdf
dc.language.iso
eng
en_US
dc.publisher
Universitat de Barcelona
dc.rights.license
ADVERTIMENT. Tots els drets reservats. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.
dc.source
TDX (Tesis Doctorals en Xarxa)
dc.subject
Disseny de medicaments
en_US
dc.subject
Diseño de medicamentos
en_US
dc.subject
Drug design
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dc.subject
Disseny assistit per ordinador
en_US
dc.subject
Diseño asistido por ordenador
en_US
dc.subject
Computer-aided design
en_US
dc.subject.other
Ciències de la Salut
en_US
dc.title
Fragment-to-Lead Optimization with Automated and Iterative Virtual Screening
en_US
dc.type
info:eu-repo/semantics/doctoralThesis
dc.type
info:eu-repo/semantics/publishedVersion
dc.subject.udc
615
en_US
dc.contributor.director
Barril Alonso, Xavier
dc.contributor.tutor
Barril Alonso, Xavier
dc.embargo.terms
12 mesos
en_US
dc.rights.accessLevel
info:eu-repo/semantics/openAccess


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