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dc.contributor.author
Button, Alexander L.
dc.contributor.supervisor
Schneider, Gisbert
dc.contributor.supervisor
Hall, Jonathan
dc.date.accessioned
2020-08-25T13:49:33Z
dc.date.available
2020-08-25T13:02:27Z
dc.date.available
2020-08-25T13:49:33Z
dc.date.issued
2020
dc.identifier.uri
http://hdl.handle.net/20.500.11850/432612
dc.identifier.doi
10.3929/ethz-b-000432612
dc.description.abstract
Drug discovery is the process of producing compounds with desired biological properties towards diseases of interest. Considering the enormous number of potential structures one could produce, the achievements seen in modern pharmaceutical science are astounding. Despite this, the immense amount of resources required to develop a drug compound presents a bottleneck in the development of pharmaceutical compounds. Computational methods, such as machine learning, offer a potential solution to this problem. Their major advantage is that, by proposing structures in silico, they can narrow the search space, reducing the number of compounds required to be synthesized and tested. However, the use of such methods is often encumbered with issues of synthetic feasibility. To aid in this issue, we present the algorithm DINGOS (Design of Innovative NCEs Generated through Optimization Strategies). DINGOS combines the predictive capabilities of computational methods with a rule-based model of synthesizability. DINGOS is a ligand-based scoring method, generating structures which are similar to the template ligand, while within the provided definition of synthesizability. The functionality of the DINGOS algorithm was demonstrated in a case-study, in which designs were proposed for four compounds (alectinib, cariprazine, osimertinib, and pimavanserin). For each template, a set of 300 designs was proposed, out of which, above 50% were predicted as active by target prediction. Three out of four selected candidate structures were successfully synthesized with the synthetic pathway predicted by DINGOS. Of three synthesized compounds, one showed activity towards the 5-HT2B serotonin receptor. The modular nature of the DINGOS algorithm was demonstrated in a series of follow-up studies. In the first follow-up study, DINGOS was paired with a custom automated synthesis system in order to generate de novo designs within an autonomous active learning drug design cycle. Two rounds of automated synthesis were performed, generating a total of 22 novel compounds, of which five showed micromolar activity towards carbonic anhydrase II. The method was further extended, first by modifying the core predictive component of the algorithm, allowing for the internal representation of the scoring function to be flexibly changed, and in the second, DINGOS was combined with a MCTS (Monte Carlo tree search) algorithm in order to allow for non-greedy optimization of the designed structures. The results of this work showcase the potential of the DINGOS algorithm as a method for generating synthetically feasible de novo designs, and highlights DINGOS’ ability to be adapted to a variety of different drug design problems, narrowing the gap between in silico and experimental drug design.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Pharmaceuticals
en_US
dc.subject
Machine learning (artificial intelligence)
en_US
dc.subject
Cheminformatics
en_US
dc.subject
Software
en_US
dc.subject
DRUG DEVELOPMENT + DRUG DESIGN + DRUG DISCOVERY (PHARMACY)
en_US
dc.title
Chemical Decision Making: An exploration into the modeling and quantification of modern drug discovery
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2020-08-25
ethz.size
171 p.
en_US
ethz.code.ddc
DDC - DDC::5 - Science::540 - Chemistry
en_US
ethz.code.ddc
DDC - DDC::6 - Technology, medicine and applied sciences::610 - Medical sciences, medicine
en_US
ethz.identifier.diss
26650
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03852 - Schneider, Gisbert / Schneider, Gisbert
en_US
ethz.date.deposited
2020-08-25T13:02:36Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-08-25T13:49:46Z
ethz.rosetta.lastUpdated
2021-02-15T16:41:23Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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