Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/53985

Título:

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

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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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Artículo

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Palabras clave:

COVID-19
Artificial intelligence
Coronavirus
Drug discovery
In silico screening
Protease
Rubbish in rubbish out
Target-based

Descriptores:

INTELIGENCIA ARTIFICIAL
CORONAVIRUS
DESCUBRIMIENTO DE DROGAS
TAMIZAJE MASIVO
PÉPTIDO HIDROLASAS
EXPECTATIVAS DE RESULTADOS

Año de publicación:

2023

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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.

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Frontiers Media

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Frontiers in Pharmacology, 2023;14

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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.

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Licencia Creative Commons Atribución (CC - By 4.0)

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