Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro
Resumen:
Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.
| 2023 | |
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COVID-19 Artificial intelligence Coronavirus Drug discovery In silico screening Protease Rubbish in rubbish out Target-based INTELIGENCIA ARTIFICIAL CORONAVIRUS DESCUBRIMIENTO DE DROGAS TAMIZAJE MASIVO PÉPTIDO HIDROLASAS EXPECTATIVAS DE RESULTADOS |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/53985 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| _version_ | 1871253157040881664 |
|---|---|
| author | Ruatta, Santiago M. |
| author2 | Prada Gori, Denis N. Fló Díaz, Martín Lorenzelli, Franca Perelmuter, Karen Alberca, Lucas N. Bellera, Carolina L. Medeiros, Andrea López, Gloria V. Ingold, Mariana Porcal, Williams Dibello, Estefanía Ihnatenko, Irina Kunick, Conrad Incerti, Marcelo Luzardo, Martín Colobbio, Maximiliano Ramos, Juan Carlos Manta, Eduardo Minini, Lucía Lavaggi, María Laura Hernández, Paola Šarlauskas, Jonas Huerta García, César Sebastián Castillo, Rafael Hernández-Campos, Alicia Ribaudo, Giovanni Zagotto, Giuseppe Carlucci, Renzo Medrán, Noelia S. Labadie, Guillermo R. Martinez-Amezaga, Maitena Delpiccolo, Carina M. L. Mata, Ernesto G. Scarone, Laura Posada, Laura Serra, Gloria Calogeropoulou, Theodora Prousis, Kyriakos Detsi, Anastasia Cabrera, Mauricio Álvarez, Guzmán Aicardo, Adrián Araújo, Verena Chavarría, Cecilia Peterlin Mašič, Lucija Gantner, Melisa E. Llanos, Manuel A. Rodríguez, Santiago Gavernet, Luciana Park, Soonju Heo, Jinyeong Lee, Honggun Paul Park, Kyu-Ho Bollati-Fogolín, Mariela Pritsch, Otto Shum, David Talevi, Alan Comini, Marcelo A. |
| author2_role | author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| author_facet | Ruatta, Santiago M. Prada Gori, Denis N. Fló Díaz, Martín Lorenzelli, Franca Perelmuter, Karen Alberca, Lucas N. Bellera, Carolina L. Medeiros, Andrea López, Gloria V. Ingold, Mariana Porcal, Williams Dibello, Estefanía Ihnatenko, Irina Kunick, Conrad Incerti, Marcelo Luzardo, Martín Colobbio, Maximiliano Ramos, Juan Carlos Manta, Eduardo Minini, Lucía Lavaggi, María Laura Hernández, Paola Šarlauskas, Jonas Huerta García, César Sebastián Castillo, Rafael Hernández-Campos, Alicia Ribaudo, Giovanni Zagotto, Giuseppe Carlucci, Renzo Medrán, Noelia S. Labadie, Guillermo R. Martinez-Amezaga, Maitena Delpiccolo, Carina M. L. Mata, Ernesto G. Scarone, Laura Posada, Laura Serra, Gloria Calogeropoulou, Theodora Prousis, Kyriakos Detsi, Anastasia Cabrera, Mauricio Álvarez, Guzmán Aicardo, Adrián Araújo, Verena Chavarría, Cecilia Peterlin Mašič, Lucija Gantner, Melisa E. Llanos, Manuel A. Rodríguez, Santiago Gavernet, Luciana Park, Soonju Heo, Jinyeong Lee, Honggun Paul Park, Kyu-Ho Bollati-Fogolín, Mariela Pritsch, Otto Shum, David Talevi, Alan Comini, Marcelo A. |
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| dc.contributor.filiacion.none.fl_str_mv | Ruatta Santiago M., Institut Pasteur de Montevideo (Uruguay) Prada Gori Denis N., Universidad Nacional de La Plata (Argentina). Facultad de Ciencias Exactas Fló Díaz Martín, Universidad de la República (Uruguay). Facultad de Medicina. Departamento de Inmunobiología Lorenzelli Franca, Institut Pasteur de Montevideo (Uruguay) Perelmuter Karen, Institut Pasteur de Montevideo (Uruguay) Alberca Lucas N., Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina) Bellera Carolina L., Universidad Nacional de La Plata (Argentina). Facultad de Ciencias Exactas Medeiros Andrea, Universidad de la República (Uruguay). Facultad de Medicina. Departamento de Bioquímica López Gloria V., Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Ingold Mariana, Institut Pasteur de Montevideo (Uruguay) Porcal Williams, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Dibello Estefanía, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Ihnatenko Irina, Technische Universität Braunschweig (Alemania). Institute of Medicinal and Pharmaceutical Chemistry Kunick Conrad, Technische Universität Braunschweig (Alemania). Institute of Medicinal and Pharmaceutical Chemistry Incerti Marcelo, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Luzardo Martín, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Colobbio Maximiliano, Universidad de la República (Uruguay). Facultad de Química. Laboratorio de Química Fina Ramos Juan Carlos, Universidad de la República (Uruguay). Facultad de Química. Laboratorio de Química Fina Manta Eduardo, Universidad de la República (Uruguay). Facultad de Química. Laboratorio de Química Fina Minini Lucía, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Lavaggi María Laura, Universidad de la República. Centro Universitario Regional Noreste. Laboratorio de Química Biológica Ambiental Hernández Paola, Instituto de Investigaciones Biológicas Clemente Estable (Uruguay). Departamento de Genética Šarlauskas Jonas, Vilnius University (Lituania). Institute of Biochemistry Huerta García César Sebastián, Universidad Nacional Autónoma de México (México). Facultad de Química. Departamento de Farmacia Castillo Rafael, Universidad Nacional Autónoma de México (México). Facultad de Química. Departamento de Farmacia Hernández-Campos Alicia, Universidad Nacional Autónoma de México (México). Facultad de Química. Departamento de Farmacia Ribaudo Giovanni, University of Brescia (Italia). Department of Molecular and Translational Medicine Zagotto Giuseppe, University of Padova (Italia). Department of Pharmaceutical and Pharmacological Sciences Carlucci Renzo, Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y Farmacéuticas Medrán Noelia S., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y Farmacéuticas Labadie Guillermo R., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y Farmacéuticas Martinez-Amezaga Maitena, Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y Farmacéuticas Delpiccolo Carina M. L., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y Farmacéuticas Mata Ernesto G., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y Farmacéuticas Scarone Laura, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Posada Laura, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Serra Gloria, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química Orgánica Calogeropoulou Theodora, National Hellenic Research Foundation (Grecia). Institute of Chemical Biology Prousis Kyriakos, National Hellenic Research Foundation (Grecia). Institute of Chemical Biology Detsi Anastasia, National Technical University of Athens (Grecia). School of Chemical Engineering Cabrera Mauricio, Universidad de la República (Uruguay). CENUR Litoral Norte. Departamento de Ciencias Biológicas Álvarez Guzmán, Universidad de la República (Uruguay). CENUR Litoral Norte. Departamento de Ciencias Biológicas Aicardo Adrián, Universidad de la República (Uruguay). Centro de Investigaciones Biomédicas Araújo Verena, Universidad de la República (Uruguay). Escuela de Nutrición Chavarría Cecilia, Universidad de la República (Uruguay). Centro de Investigaciones Biomédicas Peterlin Mašič Lucija, Universidad de Liubliana (Eslovenia). Facultad de Farmacia Gantner Melisa E., Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina) Llanos Manuel A., Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina) Rodríguez Santiago, Universidad Nacional de La Plata (Argentina). Facultad de Ciencias Exactas Gavernet Luciana, Universidad Nacional de La Plata (Argentina). Facultad de Ciencias Exactas Park Soonju, Institut Pasteur Korea (Corea). Screening Discovery Platform Heo Jinyeong, Institut Pasteur Korea (Corea). Screening Discovery Platform Lee Honggun, Institut Pasteur Korea (Corea). Screening Discovery Platform Paul Park Kyu-Ho, Institut Pasteur Korea (Corea). Screening Discovery Platform Bollati-Fogolín Mariela, Institut Pasteur de Montevideo (Uruguay) Pritsch Otto, Universidad de la República (Uruguay). Departamento de Inmunobiología Shum David, Institut Pasteur Korea (Corea). Screening Discovery Platform Talevi Alan, Universidad Nacional de La Plata (Argentina). Facultad de Ciencias Exactas Comini Marcelo A., Institut Pasteur de Montevideo (Uruguay) |
| dc.creator.none.fl_str_mv | Ruatta, Santiago M. Prada Gori, Denis N. Fló Díaz, Martín Lorenzelli, Franca Perelmuter, Karen Alberca, Lucas N. Bellera, Carolina L. Medeiros, Andrea López, Gloria V. Ingold, Mariana Porcal, Williams Dibello, Estefanía Ihnatenko, Irina Kunick, Conrad Incerti, Marcelo Luzardo, Martín Colobbio, Maximiliano Ramos, Juan Carlos Manta, Eduardo Minini, Lucía Lavaggi, María Laura Hernández, Paola Šarlauskas, Jonas Huerta García, César Sebastián Castillo, Rafael Hernández-Campos, Alicia Ribaudo, Giovanni Zagotto, Giuseppe Carlucci, Renzo Medrán, Noelia S. Labadie, Guillermo R. Martinez-Amezaga, Maitena Delpiccolo, Carina M. L. Mata, Ernesto G. Scarone, Laura Posada, Laura Serra, Gloria Calogeropoulou, Theodora Prousis, Kyriakos Detsi, Anastasia Cabrera, Mauricio Álvarez, Guzmán Aicardo, Adrián Araújo, Verena Chavarría, Cecilia Peterlin Mašič, Lucija Gantner, Melisa E. Llanos, Manuel A. Rodríguez, Santiago Gavernet, Luciana Park, Soonju Heo, Jinyeong Lee, Honggun Paul Park, Kyu-Ho Bollati-Fogolín, Mariela Pritsch, Otto Shum, David Talevi, Alan Comini, Marcelo A. |
| dc.date.accessioned.none.fl_str_mv | 2026-03-19T15:38:31Z |
| dc.date.available.none.fl_str_mv | 2026-03-19T15:38:31Z |
| dc.date.issued.none.fl_str_mv | 2023 |
| dc.description.abstract.none.fl_txt_mv | Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle. |
| dc.format.extent.es.fl_str_mv | 23 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Ruatta S, Prada Gori D, Fló Díaz M y otros. Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro. Frontiers in Pharmacology [en línea]. 2023;14. 23 p. |
| dc.identifier.doi.none.fl_str_mv | 10.3389/fphar.2023.1193282 |
| dc.identifier.eissn.none.fl_str_mv | 1663-9812 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/53985 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Frontiers Media |
| dc.relation.none.fl_str_mv | Frontiers in Pharmacology, 2023;14 |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | COVID-19 Artificial intelligence Coronavirus Drug discovery In silico screening Protease Rubbish in rubbish out Target-based |
| dc.subject.other.es.fl_str_mv | INTELIGENCIA ARTIFICIAL CORONAVIRUS DESCUBRIMIENTO DE DROGAS TAMIZAJE MASIVO PÉPTIDO HIDROLASAS EXPECTATIVAS DE RESULTADOS |
| dc.title.none.fl_str_mv | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_fdc0066b1b10ddbad342a88e49db201c |
| identifier_str_mv | Ruatta S, Prada Gori D, Fló Díaz M y otros. Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro. Frontiers in Pharmacology [en línea]. 2023;14. 23 p. 10.3389/fphar.2023.1193282 1663-9812 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
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| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/53985 |
| publishDate | 2023 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | karina.camps@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Ruatta Santiago M., Institut Pasteur de Montevideo (Uruguay)Prada Gori Denis N., Universidad Nacional de La Plata (Argentina). Facultad de Ciencias ExactasFló Díaz Martín, Universidad de la República (Uruguay). Facultad de Medicina. Departamento de InmunobiologíaLorenzelli Franca, Institut Pasteur de Montevideo (Uruguay)Perelmuter Karen, Institut Pasteur de Montevideo (Uruguay)Alberca Lucas N., Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina)Bellera Carolina L., Universidad Nacional de La Plata (Argentina). Facultad de Ciencias ExactasMedeiros Andrea, Universidad de la República (Uruguay). Facultad de Medicina. Departamento de BioquímicaLópez Gloria V., Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaIngold Mariana, Institut Pasteur de Montevideo (Uruguay)Porcal Williams, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaDibello Estefanía, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaIhnatenko Irina, Technische Universität Braunschweig (Alemania). Institute of Medicinal and Pharmaceutical ChemistryKunick Conrad, Technische Universität Braunschweig (Alemania). Institute of Medicinal and Pharmaceutical ChemistryIncerti Marcelo, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaLuzardo Martín, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaColobbio Maximiliano, Universidad de la República (Uruguay). Facultad de Química. Laboratorio de Química FinaRamos Juan Carlos, Universidad de la República (Uruguay). Facultad de Química. Laboratorio de Química FinaManta Eduardo, Universidad de la República (Uruguay). Facultad de Química. Laboratorio de Química FinaMinini Lucía, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaLavaggi María Laura, Universidad de la República. Centro Universitario Regional Noreste. Laboratorio de Química Biológica AmbientalHernández Paola, Instituto de Investigaciones Biológicas Clemente Estable (Uruguay). Departamento de GenéticaŠarlauskas Jonas, Vilnius University (Lituania). Institute of BiochemistryHuerta García César Sebastián, Universidad Nacional Autónoma de México (México). Facultad de Química. Departamento de FarmaciaCastillo Rafael, Universidad Nacional Autónoma de México (México). Facultad de Química. Departamento de FarmaciaHernández-Campos Alicia, Universidad Nacional Autónoma de México (México). Facultad de Química. Departamento de FarmaciaRibaudo Giovanni, University of Brescia (Italia). Department of Molecular and Translational MedicineZagotto Giuseppe, University of Padova (Italia). Department of Pharmaceutical and Pharmacological SciencesCarlucci Renzo, Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y FarmacéuticasMedrán Noelia S., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y FarmacéuticasLabadie Guillermo R., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y FarmacéuticasMartinez-Amezaga Maitena, Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y FarmacéuticasDelpiccolo Carina M. L., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y FarmacéuticasMata Ernesto G., Universidad Nacional de Rosario (Argentina). Facultad de Ciencias Bioquímicas y FarmacéuticasScarone Laura, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaPosada Laura, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaSerra Gloria, Universidad de la República (Uruguay). Facultad de Química. Departamento de Química OrgánicaCalogeropoulou Theodora, National Hellenic Research Foundation (Grecia). Institute of Chemical BiologyProusis Kyriakos, National Hellenic Research Foundation (Grecia). Institute of Chemical BiologyDetsi Anastasia, National Technical University of Athens (Grecia). School of Chemical EngineeringCabrera Mauricio, Universidad de la República (Uruguay). CENUR Litoral Norte. Departamento de Ciencias BiológicasÁlvarez Guzmán, Universidad de la República (Uruguay). CENUR Litoral Norte. Departamento de Ciencias BiológicasAicardo Adrián, Universidad de la República (Uruguay). Centro de Investigaciones BiomédicasAraújo Verena, Universidad de la República (Uruguay). Escuela de NutriciónChavarría Cecilia, Universidad de la República (Uruguay). Centro de Investigaciones BiomédicasPeterlin Mašič Lucija, Universidad de Liubliana (Eslovenia). Facultad de FarmaciaGantner Melisa E., Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina)Llanos Manuel A., Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina)Rodríguez Santiago, Universidad Nacional de La Plata (Argentina). Facultad de Ciencias ExactasGavernet Luciana, Universidad Nacional de La Plata (Argentina). Facultad de Ciencias ExactasPark Soonju, Institut Pasteur Korea (Corea). Screening Discovery PlatformHeo Jinyeong, Institut Pasteur Korea (Corea). Screening Discovery PlatformLee Honggun, Institut Pasteur Korea (Corea). Screening Discovery PlatformPaul Park Kyu-Ho, Institut Pasteur Korea (Corea). Screening Discovery PlatformBollati-Fogolín Mariela, Institut Pasteur de Montevideo (Uruguay)Pritsch Otto, Universidad de la República (Uruguay). Departamento de InmunobiologíaShum David, Institut Pasteur Korea (Corea). Screening Discovery PlatformTalevi Alan, Universidad Nacional de La Plata (Argentina). Facultad de Ciencias ExactasComini Marcelo A., Institut Pasteur de Montevideo (Uruguay)2026-03-19T15:38:31Z2026-03-19T15:38:31Z2023Ruatta S, Prada Gori D, Fló Díaz M y otros. Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro. Frontiers in Pharmacology [en línea]. 2023;14. 23 p.https://hdl.handle.net/20.500.12008/5398510.3389/fphar.2023.11932821663-9812Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.Submitted by Almiñana María Cecilia (marialminana@gmail.com) on 2026-03-18T12:46:30Z No. of bitstreams: 2 license_rdf: 25630 bytes, checksum: e7132498e7c1fe99f7096667baa99b25 (MD5) Garbage in, garbage out.pdf: 40138047 bytes, checksum: 1f1f6ab9041d908ec705a32db33a46bd (MD5)Approved for entry into archive by Almiñana María Cecilia (marialminana@gmail.com) on 2026-03-18T14:27:29Z (GMT) No. of bitstreams: 2 license_rdf: 25630 bytes, checksum: e7132498e7c1fe99f7096667baa99b25 (MD5) Garbage in, garbage out.pdf: 40138047 bytes, checksum: 1f1f6ab9041d908ec705a32db33a46bd (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2026-03-19T15:38:31Z (GMT). No. of bitstreams: 2 license_rdf: 25630 bytes, checksum: e7132498e7c1fe99f7096667baa99b25 (MD5) Garbage in, garbage out.pdf: 40138047 bytes, checksum: 1f1f6ab9041d908ec705a32db33a46bd (MD5) Previous issue date: 202323 p.application/pdfenengFrontiers MediaFrontiers in Pharmacology, 2023;14Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)COVID-19Artificial intelligenceCoronavirusDrug discoveryIn silico screeningProteaseRubbish in rubbish outTarget-basedINTELIGENCIA ARTIFICIALCORONAVIRUSDESCUBRIMIENTO DE DROGASTAMIZAJE MASIVOPÉPTIDO HIDROLASASEXPECTATIVAS DE RESULTADOSGarbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MProArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaRuatta, Santiago M.Prada Gori, Denis N.Fló Díaz, MartínLorenzelli, FrancaPerelmuter, KarenAlberca, Lucas N.Bellera, Carolina L.Medeiros, AndreaLópez, Gloria V.Ingold, MarianaPorcal, WilliamsDibello, EstefaníaIhnatenko, IrinaKunick, ConradIncerti, MarceloLuzardo, MartínColobbio, MaximilianoRamos, Juan CarlosManta, EduardoMinini, LucíaLavaggi, María LauraHernández, PaolaŠarlauskas, JonasHuerta García, César SebastiánCastillo, RafaelHernández-Campos, AliciaRibaudo, GiovanniZagotto, GiuseppeCarlucci, RenzoMedrán, Noelia S.Labadie, Guillermo R.Martinez-Amezaga, MaitenaDelpiccolo, Carina M. 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públicahttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestkarina.camps@seciu.edu.uyUruguayopendoar:47712026-03-19T15:50:52COLIBRI - Universidad de la Repúblicafalse |
| spellingShingle | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro Ruatta, Santiago M. COVID-19 Artificial intelligence Coronavirus Drug discovery In silico screening Protease Rubbish in rubbish out Target-based INTELIGENCIA ARTIFICIAL CORONAVIRUS DESCUBRIMIENTO DE DROGAS TAMIZAJE MASIVO PÉPTIDO HIDROLASAS EXPECTATIVAS DE RESULTADOS |
| status_str | publishedVersion |
| title | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| title_full | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| title_fullStr | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| title_full_unstemmed | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| title_short | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| title_sort | Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro |
| topic | COVID-19 Artificial intelligence Coronavirus Drug discovery In silico screening Protease Rubbish in rubbish out Target-based INTELIGENCIA ARTIFICIAL CORONAVIRUS DESCUBRIMIENTO DE DROGAS TAMIZAJE MASIVO PÉPTIDO HIDROLASAS EXPECTATIVAS DE RESULTADOS |
| url | https://hdl.handle.net/20.500.12008/53985 |
