ABSTRACT
Many natural substances and drugs are radical scavengers that prevent the oxidative damage to fundamental cell components. This process may occur via different mechanisms, among which, one of the most important, is hydrogen atom transfer. The feasibility of this process can be assessed in silico using quantum mechanics to compute ΔGHAT â. This approach is accurate, but time consuming. The use of machine learning (ML) allows us to reduce tremendously the computational cost of the assessment of the scavenging properties of a potential antioxidant, almost without affecting the quality of the results. However, in many ML implementations, the description of the relevant features of a molecule in a machine-friendly language is still the most challenging aspect. In this work, we present a newly developed machine-readable molecular representation aimed at the application of automatized ML algorithms. In particular, we show an application on the calculation of ΔGHAT â.
Subject(s)
Antioxidants/chemistry , Free Radical Scavengers/chemistry , Machine Learning , Models, Chemical , Quantum Theory , Antioxidants/metabolism , Antioxidants/pharmacology , Fluoxetine/analogs & derivatives , Fluoxetine/chemistry , Fluoxetine/pharmacology , Free Radical Scavengers/metabolism , Free Radical Scavengers/pharmacology , ThermodynamicsABSTRACT
The interest in diphenyl ditelluride (Ph2Te2) is related to its strict analogy to diphenyl diselenide (Ph2Se2), whose capacity to reduce organic peroxides is largely exploited in catalysis and green chemistry. Since the latter is also a promising candidate as an antioxidant drug and mimic of the ubiquitous enzyme glutathione peroxidase (GPx), the use of organotellurides in medicinal chemistry is gaining importance, despite the fact that tellurium has no recognized biological role and its toxicity must be cautiously pondered. Both Ph2Se2 and Ph2Te2 exhibit significant conformational freedom due to the softness of the inter-chalcogen and carbonâ»chalcogen bonds, preventing the existence of a unique structure in solution. Therefore, the accurate calculation of the NMR chemical shifts of these flexible molecules is not trivial. In this study, a detailed structural analysis of Ph2Te2 is carried out using a computational approach combining classical molecular dynamics and relativistic density functional theory methods. The goal is to establish how structural changes affect the electronic structure of diphenyl ditelluride, particularly the 125Te chemical shift.
Subject(s)
Benzene Derivatives/chemistry , Organometallic Compounds/chemistry , Tellurium/chemistry , Magnetic Resonance Spectroscopy/methods , Molecular Conformation , Molecular Dynamics Simulation , Organoselenium Compounds/chemistry , Quantum Theory , Structure-Activity Relationship , ThermodynamicsABSTRACT
Zolpidem (N,N-Dimethyl-2-[6-methyl-2-(4-methylphenyl)imidazo[1,2-a]pyridin-3-yl]acetamide) is a well-known drug for the treatment of sleeping disorders. Recent literature reports on positive effects of zolpidem therapy on improving renal damage after cisplatin and on reducing akinesia without sleep induction. This has been ascribed to the antioxidant and neuroprotective capacity of this molecule, and tentatively explained according to a generic structural similarity between zolpidem and melatonin. In this work, we investigate in silico the antioxidant potential of zolpidem as scavenger of five ROSs, acting via hydrogen atom transfer (HAT) mechanism; computational methodologies based on density functional theory are employed. For completeness, the analysis is extended to six metabolites. Thermodynamic and kinetic results disclose that indeed zolpidem is an efficient radical scavenger, similarly to melatonin and Trolox, supporting the biomedical evidence that the antioxidant potential of zolpidem therapy may have a beneficial effect against oxidative injury, which is emerging as an important etiopathogenesis in numerous severe diseases, including psychiatric disorders.
