The combination of computational methods and experimental data from Nuclear Magnetic Resonance (NMR) is a considerably valuable tool in the elucidation of new natural product structures and, also, in the structural revision of previously reported compounds. Until recently, only classical statistical parameters were used, for example, linear correlation coefficient (R 2 ), mean absolute error (MAE), or root mean square deviation (RMSD), as a way to statistically "validate" the structure pointed out by experimental NMR spectra. Regarding the resolution of the relative configuration of organic molecules, novel tools were available in the last few years to assist in the NMR elucidation process. The most relevant are DP4+, which is based on a Bayesian probabil-

Recently, several users of nuclear magnetic resonance spectroscopy have considered the employing of quantum chemical methods in the spectra predictions, since these methods are now well developed and implemented in popular program packages. Based on the experience of our group, the purpose of this article is to test the feasibility in the generating of a scaling factor for hydrogen-1 chemical shifts calculations at the GIAO-mPW1PW91/6-31G(d)//mPW1PW91/6-31G(d) level of theory in gas phase to assist in the determination of organic molecule structures. It is important to highlight that our level of theory requires low computational time, consequently it can be used even in personal computers. We used 80 organic molecules to yield a scaling factor equation: scaled chemical is equal to 0.98 (calculated chemical shift)+0.09, (calculated tetramethylsilane value of 32.26 parts per million). The test molecule is oleana-12(13), 15(16)-dienoic acid, a triterpene with a complex structure, and with various biological and pharmacological applications. The error values of root mean square were slightly higher for the triterpene molecule compared to the 80 molecules (1.40 percent and 1.53 percent, respectively. We believe that this was due to the greater flexibility of the triterpene molecule. Thus, taking into consideration the cost-effectiveness ratio, the 1H NMR calculations at the GIAO-mPW1PW91/6-31G(d)//mPW1PW91/6-31G(d) level of theory have produced promissory results.

For the development of drugs that treat SARS-CoV-2, the fastest way is to find potential molecules from drugs already on the market. Unfortunately, there is currently no specific drug or treatment for COVID-19. Among all structural proteins in SARS-CoV, the spike protein is the main antigenic component responsible for inducing host immune responses, neutralizing antibodies, and/or protecting immunity against virus infection. Molecular docking is a technique used to predict whether a molecule will bind to another. It is usually a protein to another or a protein to a binding compound. Natural products are potential binders in several studies involving coronavirus. The structure of the ligand plays a fundamental role in its biological properties. The nuclear magnetic resonance technique is one of the most powerful tools for the structural determination of ligands from the origin of natural products. Nowadays, molecular modeling is an important accessory tool to experimentally got nuclear magnetic resonance data. In the present work, molecular docking studies aimed is to investigate the limiting affinities of trans-dehydrocrotonin molecule and to identify the main amino acid residues that could play a fundamental role in their mechanism of action of the SARS-CoV spike protein. Another aim of this work is all about to evaluate 10 hybrid functionalities, along with three base pairs using computational programs to discover which ones are more reliable with the experimental result the best computational method to study organic compounds. We compared the results between the mean absolute deviation (MAD) and root-mean-square deviation (RMSD) of the molecules, and the smallest number between them was the best result. The positions assumed by the ligands in the active site of the spike glycoprotein allow assuming associations with different local amino acids.

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This is a theoretical-experimental work, where the focus molecule of the study is savinine, a lignan of the dibenzylbutyrolactonic type, substances that can be found in several genera, one of which has a greater occurrence is the genus Acanthopanax (Araliaceae) which is traditionally used as an analgesic and immune system stimulant, in addition to exhibiting a potent insecticidal and cytotoxic activity for human colon carcinoma HCT116 cells. It was isolated and here we present its experimental and theoretical characterization by means of 13C and 1H NMR data and the possible confirmation of the structure using the neural network tool (ANN-PRA). The objective of this work is to use theoretical calculations of 13C and 1H NMR and experimental data for the resolution of the savinine structure, and the use of the neural network tool (ANN-PRA) to confirm the structure of the molecule.