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An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme

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Abstract

Nowadays, the importance of computational methods in the design of therapeutic agents in a more efficient way is indisputable. Particularly, these methods have been important in the design of novel acetylcholinesterase enzyme inhibitors related to Alzheimer’s disease. In this sense, in this report a computational model of linear prediction of acetylcholinesterase inhibitory activity of steroids and triterpenes is presented. The model is based in a correlation between binding energies obtained from molecular dynamic simulations (after docking studies) and \(\hbox {IC}_{{50}}\) values of a training set. This set includes a family of natural and semi-synthetic structurally related alkaloids reported in bibliography. These types of compounds, with some structural complexity, could be used as building blocks for the synthesis of many important biologically active compounds Therefore, the present study proposes an alternative based on the use of conventional and easily accessible tools to make progress on the rational design of molecules with biological activity.

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Acknowledgements

We would like to thank E. Daiann Sosa Carrizo for helpful comments and discussions and F. Brigante for the revision of this manuscript. This work was supported in part by the Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), the Agencia Nacional de Promoción Científica y Tecnológica (FONCYT, Argentina), the Agencia Córdoba Ciencia and the Secretaría de Ciencia y Técnica (SECYT) of the Universidad Nacional de Córdoba. INFIQC and IMBIV are jointly sponsored by CONICET and the Universidad Nacional de Córdoba. All calculations were performed with computational resources from the Centro de Computación de Alto Desempeño—Universidad Nacional de Córdoba (http://ccad.unc.edu.ar/), in particular the Mendieta Cluster that belongs to the Facultad de Matemática, Astronomía y Física, that is also part of SNCAD, República Argentina.

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  1. INFIQC-CONICET, Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, X5000HUA, Córdoba, Argentina

    José L. Borioni, Adriana B. Pierini, Alicia B. Peñéñory & Marcelo Puiatti

  2. INQUISUR-CONICET, Departamento de Química, Universidad Nacional del Sur, B8000CPB, Bahía Blanca, Argentina

    Valeria Cavallaro & Ana P. Murray

  3. IMBIV-CONICET, Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, X5000HUA, Córdoba, Argentina

    Manuela E. García

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  1. José L. Borioni
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Borioni, J.L., Cavallaro, V., Pierini, A.B. et al. An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme. J Comput Aided Mol Des 34, 1079–1090 (2020). https://doi.org/10.1007/s10822-020-00324-y

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