Crystal structure, quantum chemical insights, and molecular docking studies of Naryl-2-(N-disubstituted) acetamide compounds: potential inhibitors for neurodegenerative enzymes

Camargo-Ayala, Lorena; Bedoya, Mauricio; Prent-Peñaloza, Luis; Polo-Cuadrado, Efraín; Osorio, Edison; Brito, Iván; Delgado, Gerzon E.; González, Wendy; Gutierrez, Margarita

doi:10.1039/d3ra08649f

RSC Adv.

2024

DOI: 10.1039/d3ra08649f

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Crystal structure, quantum chemical insights, and molecular docking studies of Naryl-2-(N-_{disubstituted}) acetamide compounds: potential inhibitors for neurodegenerative enzymes

Lorena Camargo-Ayala,

Mauricio Bedoya,

Luis Prent-Peñaloza

et al.

Abstract: The increase in and concern about neurodegenerative diseases continue to grow in an increasingly long-lived world population.

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SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

de Azevedo,

Quiroga,

Villarreal

et al. 2024

J Comput Chem

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Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein–ligand structures and affinity data make it possible to develop machine‐learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit‐Learn to calculate binding affinity based on protein–ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine‐learning models based on crystal, docked, and AlphaFold‐generated structures. As a proof of concept, we examine the performance of SAnDReS‐generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS‐generated models showed predictive performance close to or better than other machine‐learning models such as KDEEP, CSM‐lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

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SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

de Azevedo,

Quiroga,

Villarreal

et al. 2024

J Comput Chem

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Lycium ruthenicum Murray anthocyanin-driven neuroprotection modulates the gut microbiome and metabolome of MPTP-treated mice

Cao,

Tian,

Chu

et al. 2024

Food Funct.

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Crystal structure, quantum chemical insights, and molecular docking studies of Naryl-2-(N-_{disubstituted}) acetamide compounds: potential inhibitors for neurodegenerative enzymes

Abstract: The increase in and concern about neurodegenerative diseases continue to grow in an increasingly long-lived world population.

Cited by 2 publications

References 48 publications

SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

Lycium ruthenicum Murray anthocyanin-driven neuroprotection modulates the gut microbiome and metabolome of MPTP-treated mice

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