Quantum chemical approaches in structure-based virtual screening and lead optimization
- Autores
- Cavasotto, Claudio Norberto; Adler, Natalia Sol; Aucar, María Gabriela
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.
Fil: Cavasotto, Claudio Norberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina
Fil: Adler, Natalia Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina
Fil: Aucar, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina - Materia
-
BINDING FREE ENERGY
DRUG LEAD OPTIMIZATION
MOLECULAR DOCKING
MOLECULAR DYNAMICS
QUANTUM MECHANICS
SEMI-EMPIRICAL METHODS
STRUCTURE-BASED DRUG DESIGN - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/91266
Ver los metadatos del registro completo
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spelling |
Quantum chemical approaches in structure-based virtual screening and lead optimizationCavasotto, Claudio NorbertoAdler, Natalia SolAucar, María GabrielaBINDING FREE ENERGYDRUG LEAD OPTIMIZATIONMOLECULAR DOCKINGMOLECULAR DYNAMICSQUANTUM MECHANICSSEMI-EMPIRICAL METHODSSTRUCTURE-BASED DRUG DESIGNhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.Fil: Cavasotto, Claudio Norberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Adler, Natalia Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Aucar, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFrontiers Research Foundation2018-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/91266Cavasotto, Claudio Norberto; Adler, Natalia Sol; Aucar, María Gabriela; Quantum chemical approaches in structure-based virtual screening and lead optimization; Frontiers Research Foundation; Frontiers in Chemistry; 6; MAY; 5-2018; 1-72296-2646CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fchem.2018.00188/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fchem.2018.00188info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:01:58Zoai:ri.conicet.gov.ar:11336/91266instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:01:59.274CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
title |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
spellingShingle |
Quantum chemical approaches in structure-based virtual screening and lead optimization Cavasotto, Claudio Norberto BINDING FREE ENERGY DRUG LEAD OPTIMIZATION MOLECULAR DOCKING MOLECULAR DYNAMICS QUANTUM MECHANICS SEMI-EMPIRICAL METHODS STRUCTURE-BASED DRUG DESIGN |
title_short |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
title_full |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
title_fullStr |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
title_full_unstemmed |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
title_sort |
Quantum chemical approaches in structure-based virtual screening and lead optimization |
dc.creator.none.fl_str_mv |
Cavasotto, Claudio Norberto Adler, Natalia Sol Aucar, María Gabriela |
author |
Cavasotto, Claudio Norberto |
author_facet |
Cavasotto, Claudio Norberto Adler, Natalia Sol Aucar, María Gabriela |
author_role |
author |
author2 |
Adler, Natalia Sol Aucar, María Gabriela |
author2_role |
author author |
dc.subject.none.fl_str_mv |
BINDING FREE ENERGY DRUG LEAD OPTIMIZATION MOLECULAR DOCKING MOLECULAR DYNAMICS QUANTUM MECHANICS SEMI-EMPIRICAL METHODS STRUCTURE-BASED DRUG DESIGN |
topic |
BINDING FREE ENERGY DRUG LEAD OPTIMIZATION MOLECULAR DOCKING MOLECULAR DYNAMICS QUANTUM MECHANICS SEMI-EMPIRICAL METHODS STRUCTURE-BASED DRUG DESIGN |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.4 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline. Fil: Cavasotto, Claudio Norberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina Fil: Adler, Natalia Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina Fil: Aucar, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina |
description |
Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/91266 Cavasotto, Claudio Norberto; Adler, Natalia Sol; Aucar, María Gabriela; Quantum chemical approaches in structure-based virtual screening and lead optimization; Frontiers Research Foundation; Frontiers in Chemistry; 6; MAY; 5-2018; 1-7 2296-2646 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/91266 |
identifier_str_mv |
Cavasotto, Claudio Norberto; Adler, Natalia Sol; Aucar, María Gabriela; Quantum chemical approaches in structure-based virtual screening and lead optimization; Frontiers Research Foundation; Frontiers in Chemistry; 6; MAY; 5-2018; 1-7 2296-2646 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fchem.2018.00188/full info:eu-repo/semantics/altIdentifier/doi/10.3389/fchem.2018.00188 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Research Foundation |
publisher.none.fl_str_mv |
Frontiers Research Foundation |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.22299 |