An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme
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An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme José L. Borioni1 · Valeria Cavallaro2 · Adriana B. Pierini1 · Ana P. Murray2 · Alicia B. Peñéñory1 · Marcelo Puiatti1 · Manuela E. García3 Received: 21 January 2020 / Accepted: 14 June 2020 © Springer Nature Switzerland AG 2020
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 IC50 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. Keywords Biological activity prediction · Acetylcholinesterase inhibitors · Steroidal and triterpenoidal compounds · Molecular dynamic simulations · Structure activity relationships
Introduction Dementia syndrome is related to a large number of underlying brain pathologies. The World Health Organization has suggested, that by 2040, the neurodegenerative disorders Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10822-020-00324-y) contains supplementary material, which is available to authorized users. * José L. Borioni [email protected] * Manuela E. García 1
INFIQC‑CONICET, Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, X5000HUA Córdoba, Argentina
2
INQUISUR‑CONICET, Departamento de Química, Universidad Nacional del Sur, B8000CPB Bahía Blanca, Argentina
3
IMBIV‑CONICET, Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, X5000HUA Córdoba, Argentina
will become the second-most cause of death after cardiovascular diseases [1]. In this context, Alzheimer’s disease (AD) is the most common of dementia affecting an important part of the population over 65 years old around the world [2–4]. This disease is characterized by cognitive dysfunction and progressive behavior skills impairment [5–8]. Because this affliction represents a great problem to the public health and a huge economic burden, many efforts and resources are invested in the search for new treatments. The etiology of this disease is not limited to a single gene or pathway, but t
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