Machine learning methods to predict the crystallization propensity of small organic molecules
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2020 |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/110180 |
Resumo: | Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under grant UID/QUI/50006/2019 (provided to the Associate Laboratory for Green Chemistry LAQV) is greatly appreciated. Florbela Pereira thanks Fundacao para a Ciencia e a Tecnologia, MCTES, for the Norma transitoria DL 57/2016 Program Contract. |
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Machine learning methods to predict the crystallization propensity of small organic moleculesChemistry(all)Materials Science(all)Condensed Matter PhysicsFundacao para a Ciencia e Tecnologia (FCT) Portugal, under grant UID/QUI/50006/2019 (provided to the Associate Laboratory for Green Chemistry LAQV) is greatly appreciated. Florbela Pereira thanks Fundacao para a Ciencia e a Tecnologia, MCTES, for the Norma transitoria DL 57/2016 Program Contract.Machine learning (ML) algorithms were explored for the prediction of the crystallization propensity based on molecular descriptors and fingerprints generated from 2D chemical structures and 3D molecular descriptors from 3D chemical structures optimized with empirical methods. In total, 57 815 molecules were retrieved from the Reaxys® database, from those 53 998 molecules are recorded as crystalline (class A), 3097 as polymorphic (class B), and 720 as amorphous (class C). A training data set with 40 462 organic molecules was used to build the models, which were validated with an external test set comprising 17 353 organic molecules. Several ML algorithms such as random forest (RF), support vector machines (SVM), and deep learning multilayer perceptron networks (MLP) were screened. The best performance was achieved with a consensus classification model obtained by RF, SVM, and MLP models, which predicted the external test set with an overall predictive accuracy (Q) of up to 80%.LAQV@REQUIMTEDQ - Departamento de QuímicaRUNPereira, Florbela2022-03-31T00:31:40Z2020-04-282020-04-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/110180eng1466-8033PURE: 18074508https://doi.org/10.1039/d0ce00070ainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:54:11Zoai:run.unl.pt:10362/110180Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:41:35.115955Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| title |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| spellingShingle |
Machine learning methods to predict the crystallization propensity of small organic molecules Pereira, Florbela Chemistry(all) Materials Science(all) Condensed Matter Physics |
| title_short |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| title_full |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| title_fullStr |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| title_full_unstemmed |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| title_sort |
Machine learning methods to predict the crystallization propensity of small organic molecules |
| author |
Pereira, Florbela |
| author_facet |
Pereira, Florbela |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
LAQV@REQUIMTE DQ - Departamento de Química RUN |
| dc.contributor.author.fl_str_mv |
Pereira, Florbela |
| dc.subject.por.fl_str_mv |
Chemistry(all) Materials Science(all) Condensed Matter Physics |
| topic |
Chemistry(all) Materials Science(all) Condensed Matter Physics |
| description |
Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under grant UID/QUI/50006/2019 (provided to the Associate Laboratory for Green Chemistry LAQV) is greatly appreciated. Florbela Pereira thanks Fundacao para a Ciencia e a Tecnologia, MCTES, for the Norma transitoria DL 57/2016 Program Contract. |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020-04-28 2020-04-28T00:00:00Z 2022-03-31T00:31:40Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/110180 |
| url |
http://hdl.handle.net/10362/110180 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
1466-8033 PURE: 18074508 https://doi.org/10.1039/d0ce00070a |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
10 application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138028948553728 |