dc.contributor.author | Carracedo Reboredo, Paula | |
dc.contributor.author | Aranzamendi Uruburu, Eider | |
dc.contributor.author | He, Shan | |
dc.contributor.author | Arrasate Gil, Sonia | |
dc.contributor.author | Munteanu, Cristian R. | |
dc.contributor.author | Fernández Lozano, Carlos | |
dc.contributor.author | Sotomayor Anduiza, María Nuria | |
dc.contributor.author | Lete Expósito, María Esther | |
dc.date.accessioned | 2024-01-24T13:03:47Z | |
dc.date.available | 2024-01-24T13:03:47Z | |
dc.date.issued | 2024-01-23 | |
dc.identifier.citation | Journal of Cheminformatics 16 : (2024) // Art. N. 9 | es_ES |
dc.identifier.issn | 1758-2946 | |
dc.identifier.uri | http://hdl.handle.net/10810/64282 | |
dc.description.abstract | The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production
of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this
type of reactions. The selection and design of new CPA catalysts for diferent enantioselective reactions has a dual
interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this
process is difcult and time consuming if approached from an experimental trial and error perspective. In this work,
an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model
for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2=0.96 overall
for training and validation series. It involved a Monte Carlo sampling of>100,000 pairs of query and reference reac‑
tions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation
reactions using BINOL-derived N-trifylphosphoramides as CPA catalysts is reported as a case of study. The model
was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity
Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly
online computational tool would enable sustainable optimization of reaction conditions that could lead to the design
of new CPA catalysts along with new organic synthesis products. | es_ES |
dc.description.sponsorship | Ministerio de Ciencia e Innovación ( PID2019104148 GB-I00; PID2022-137365NB-I00), Gobierno Vasco IT1558-22 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | BMC | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2019-104148GB-I00 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2022-137365NB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | chiral phosphoric acid catalysts | es_ES |
dc.subject | cheminformatics | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | amidoalkylation | es_ES |
dc.subject | asymmetric catalysis | es_ES |
dc.title | MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License | es_ES |
dc.relation.publisherversion | https://doi.org/10.1186/s13321-024-00802-7 | es_ES |
dc.identifier.doi | 10.1186/s13321-024-00802-7 | |
dc.departamentoes | Química Orgánica e Inorgánica | es_ES |
dc.departamentoeu | Kimika Organikoa eta Ez-Organikoa | es_ES |