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Mini-Reviews in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

Review Article

Medicinal Chemistry Meets Electrochemistry: Redox Potential in the Role of Endpoint or Molecular Descriptor in QSAR/QSPR

Author(s): Karel Nesměrák*

Volume 20 , Issue 14 , 2020

Page: [1341 - 1356] Pages: 16

DOI: 10.2174/1389557520666200204121806

Price: $65

Abstract

Many biochemical reactions are based on redox reactions. Therefore, the redox potential of a chemical compound may be related to its therapeutic or physiological effects. The study of redox properties of compounds is a domain of electrochemistry. The subject of this review is the relationship between electrochemistry and medicinal chemistry, with a focus on quantifying these relationships. A summary of the relevant achievements in the correlation between redox potential and structure, therapeutic activity, resp., is presented. The first part of the review examines the applicability of QSPR for the prediction of redox properties of medically important compounds. The second part brings the exhaustive review of publications using redox potential as a molecular descriptor in QSAR of biological activity. Despite the complexity of medicinal chemistry and biological reactions, it is possible to employ redox potential in QSAR/QSPR. In many cases, this electrochemical parameter plays an essential but rarely absolute role.

Keywords: Computational chemistry, electrochemistry, electron transfer, medicinal chemistry, QSAR, QSPR.

Graphical Abstract
[1]
Scotti, M.T.; Emerenciano, V.; Ferreira, M.J.P.; Scotti, L.; Stefani, R.; Da Silva, M.S.; Mendonça Jr, F.J.B. Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family. Molecules, 2012, 17(4), 4684-4702.
[http://dx.doi.org/10.3390/molecules17044684]
[2]
Nesmerak, K.; Toropov, A.A.; Toropova, A.P.; Kohoutova, P.; Waisser, K. SMILES-based quantitative structure-property relationships for half-wave potential of N-benzylsalicylthioamides. Eur. J. Med. Chem., 2013, 67, 111-114.
[http://dx.doi.org/10.1016/j.ejmech.2013.05.031]
[3]
Speck-Planche, A.; Scotti, M.T.; de Paulo-Emerenciano, V. Current pharmaceutical design of antituberculosis drugs: Future perspectives. Curr. Pharm. Des., 2010, 16(24), 2656-2665.
[http://dx.doi.org/10.2174/138161210792389289]
[4]
Achary, P.G.R.; Begum, S.; Toropova, A.P.; Toropov, A.A. A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization. Mater. Discovery, 2016, 5, 22-28.
[http://dx.doi.org/10.1016/j.md.2016.12.003]
[5]
Veselinović, J.B.; Toropov, A.A.; Toropova, A.P.; Nikolić, G.M.; Veselinović, A.M. Monte Carlo method based QSAR modeling of penicillins binding to human serum proteins. Arch. Pharm, 2015, 348(1), 62-67.
[http://dx.doi.org/10.1002/ardp.201400259]
[6]
Nimbhal, M.; Bagri, K.; Kumar, P.; Kumar, A. The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators. Struct. Chem., 2020, 1(2), 831-839.
[http://dx.doi.org/10.1007/s11224-019-01468-w]
[7]
Velázquez-Libera, J.L.; Caballero, J.; Toropova, A.P.; Toropov, A.A. Estimation of 2D autocorrelation descriptors and 2D Monte Carlo descriptors as a tool to build up predictive models for acetylcholinesterase (AChE) inhibitory activity. Chemom. Intell. Lab. Syst., 2019, 184, 14-21.
[http://dx.doi.org/10.1016/j.chemolab.2018.11.008]
[8]
Alberca, L.N.; Sbaraglini, M.L.; Morales, J.F.; Dietrich, R.; Ruiz, M.D.; Martínez, A.M.P.; Miranda, C.G.; Fraccaroli, L.; Alba Soto, C.D.; Carrillo, C.; Palestro, P.H.; Talevi, A. Cascade ligand- and structure-based virtual screening to identify new trypanocidal compounds inhibiting putrescine uptake. Front. Cell. Infect. Microbiol., 2018, 8, 173.
[http://dx.doi.org/10.3389/fcimb.2018.00173]

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