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Optimizing chemical reaction conditions with artificial intelligence

2022-09Tese de doutoramento Acesso embargado
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datacite.subject.fosCiências Naturais::Ciências Químicaspt_PT
dc.contributor.advisorRodrigues, Tiago Correia de Oliveira
dc.contributor.advisorMoreira, Rui Ferreira Alves
dc.contributor.advisorAtaíde, Filipe André Prata
dc.contributor.authorAlmeida, A. Filipa
dc.date.accessioned2023-06-28T11:04:37Z
dc.date.embargo2026-07
dc.date.issued2022-09
dc.date.submitted2022-06
dc.description.abstractThe synthesis of molecules with desired properties is an important part of some areas of science and technology, including drug discovery, chemical biology, materials science, and engineering. The execution of synthetic chemistry requires expert chemical knowledge, which is acquired over several years of hands-on laboratory practice. Finding the conditions that improve the process is a time-consuming task but essential to reduce the associated cost. Artificial Intelligence (AI) and Machine learning (ML) have emerged as a problem-solving paradigm by recognizing complex patterns in data and making predictions on future data. The improvement in computer power, challenging data and algorithms provide a basis to create methods and tools to explore numerous topics/areas, such as molecule design, retrosynthetic planning, prediction of reaction outcomes, and laboratory automation. AI/ML has also seen applications in the optimization of reaction protocols. Reaction optimization is fundamental to synthetic chemistry, including the optimization of the quality, yield, or the number of steps of the process to selecting conditions for the preparation of organic compounds. AI/ML provides efficient methods to study multi-dimensional experimental spaces and accelerate the identification of the set of optimum conditions. The continuous flow technology has emerged as one of the most important techniques in green engineering. This technology offers several benefits compared to batch synthesis, including higher safety and process control that are governed by more efficient mass and heat transfer. Together, these enable the execution of reactions with hazardous components, the intensification of the process by increasing efficiency, reducing energy consumption, cost, volume and waste and direct scalability. The optimization of chemical reactions using AI/ML algorithms combined with the continuous flow can significantly reduce the overall process development time. In this work, three types of chemical reactions relevant to the pharmaceutical industry were studied: (i) Friendländer reaction (ii) Lithiation reaction, and (iii) Fluorination reaction. Due to the conditions that are required by the Lithiation and fluorinated reactions these examples were studied in continuous flow. Using tool molecules selected from the literature, it was investigated and discussed the impact of AI/ML in the optimization of reaction protocols by leveraging a bespoke random forest (RF) for reaction space exploration and synthesis protocol optimization, applied to different chemistries. To monitor and visualize the navigation of chemical space manifold learning – t-Distributed Stochastic Neighbor Embedding (t-SNE) – was used. The evaluation of the predictive power of the models was achieved by 10-fold crossvalidation and Y-randomization studies as a naïve baseline. The RF model showed better performance compared to baselines with and without regularization (linear, ridge and lasso regression). The results suggest that the RF can quickly identify optimal reaction conditions in a discretized search space by executing less than 5% of all possible reactions.pt_PT
dc.description.provenanceSubmitted by Paula Guerreiro (passarinho@reitoria.ulisboa.pt) on 2023-05-25T11:13:24Z No. of bitstreams: 1 scnd741217_td_Andreia_Almeida.pdf: 15096134 bytes, checksum: 876dad58dabbccd04cf2b851b38b4756 (MD5)en
dc.description.provenanceMade available in DSpace on 2023-06-28T11:04:37Z (GMT). No. of bitstreams: 1 scnd741217_td_Andreia_Almeida.pdf: 15096134 bytes, checksum: 876dad58dabbccd04cf2b851b38b4756 (MD5) Previous issue date: 2022-09en
dc.identifier.tid101593414pt_PT
dc.identifier.urihttp://hdl.handle.net/10451/58381
dc.language.isoengpt_PT
dc.relationOptimizing chemistry in flow with artificial intelligence
dc.relationOptimizing chemistry in flow with artificial intelligence
dc.subjectInteligência Artificialpt_PT
dc.subjectMáquina de aprendizagempt_PT
dc.subjectOtimização químicapt_PT
dc.subjectQuímica sintéticapt_PT
dc.subjectQuímica em fluxopt_PT
dc.subjectArtificial Intelligencept_PT
dc.subjectMachine learningpt_PT
dc.subjectOptimization chemistrypt_PT
dc.subjectsynthetic chemistrypt_PT
dc.subjectFlow chemistrypt_PT
dc.titleOptimizing chemical reaction conditions with artificial intelligencept_PT
dc.typedoctoral thesis
dspace.entity.typePublication
oaire.awardNumberPD/BD/143125/2019
oaire.awardNumberCOVID/BD/152556/2022
oaire.awardTitleOptimizing chemistry in flow with artificial intelligence
oaire.awardTitleOptimizing chemistry in flow with artificial intelligence
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//PD%2FBD%2F143125%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//COVID%2FBD%2F152556%2F2022/PT
person.familyNameDaniel de Almeida
person.givenNameAndreia Filipa
person.identifier.ciencia-idC616-BCB1-972E
person.identifier.orcid0000-0002-8399-0710
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.contributor.authoremailrepositorio@reitoria.ulisboa.pt
rcaap.rightsembargoedAccesspt_PT
rcaap.typedoctoralThesispt_PT
relation.isAuthorOfPublication50863757-f0e0-4346-b5fc-207924752fa8
relation.isAuthorOfPublication.latestForDiscovery50863757-f0e0-4346-b5fc-207924752fa8
relation.isProjectOfPublication54246aaa-bd55-495d-a7a8-1613159cb172
relation.isProjectOfPublicationfe89c707-74b2-49bb-ac0a-864a8edd0406
relation.isProjectOfPublication.latestForDiscoveryfe89c707-74b2-49bb-ac0a-864a8edd0406
thesis.degree.nameTese de doutoramento, Farmácia (Química Farmacêutica e Terapêutica), Universidade de Lisboa, Faculdade de Farmácia, 2022pt_PT

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