Spice plants have a great influence on world history. For centuries, different civilizations have used them to condiment the foods of kings and nobles and applied them as embalming preservatives, perfumes, cosmetics, and medicines in different regions of the world. In general, these plants have formed the basis of traditional medicine and some of their derived substances have been utilized to treat different human diseases. Essential oils (EOs) obtained from these plants have been also used as therapeutic agents and have shown supportive uses in remedial practices. The discovery and development of bioactive compounds from these natural products, based on their traditional uses, play an important role in developing the scientific evidence of their potential pharmaceutical, cosmetic, and food applications. In the present review, using recent studies, we exhibit a general overview of the main aspects related to the importance of spice plants widely used in traditional medicine: Cinnamomum zeylanicum (true cinnamon), Mentha piperita (peppermint), Ocimum basilicum (basil), Origanum vulgare (oregano), Piper nigrum (black pepper), Rosmarinus officinalis (rosemary), and Thymus vulgaris (thyme); and we discuss new findings of the bioactive compounds obtained from their EOs, their potential applications, as well as their molecular mechanisms of action, focusing on their antioxidant activity. We also exhibit the main in vitro methods applied to determine the antioxidant activities of these natural products.

Understanding the antioxidant activity of flavonoids is important to investigate their biological activities as well as to design novel molecules with low toxicity and high activity. Aromaticity is a chemical property found in cyclic structures that plays an important role in their stability and reactivity, and its investigation can help us to understand the antioxidant activity of some heterocyclic compounds. In the present study, we applied the density functional theory (DFT) to investigate the properties of seven flavonoid structures with well-reported antioxidant activity: flavan, anthocyanidin, flavanone, flavonol, isoflavone, flavone, and flavan-3-ol. Conformational, structural, magnetic, and electronic analyses were performed using nuclear magnetic resonance, ionization potentials, electron affinity, bond dissociation energy, proton affinity, frontier molecular orbitals (highest occupied molecular orbital (HOMO)/lowest unoccupied molecular orbital (LUMO)), and aromaticity through nucleus-independent chemical shifts to analyze these seven flavonoid structures. We revised the influence of hydroxyl groups on the properties of flavonoids and also investigated the influence of the aromaticity of these seven flavonoids on the antioxidant activity.

Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyondchemicalrules-basedframework forCPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred.

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Odorant-binding proteins (OBPs) are the main olfactory proteins of mosquitoes, and their structures have been widely explored to develop new repellents. In the present study, we combined ligand- and structure-based virtual screening approaches using as a starting point 1633 compounds from 71 botanical families obtained from the Essential Oil Database (EssOilDB). Using as reference the crystallographic structure of N,N-diethyl-meta-toluamide interacting with the OBP1 homodimer of Anopheles gambiae (AgamOBP1), we performed a structural and pharmacophoric similarity search to select potential natural products from the library. Thymol acetate, 4-(4-methyl phenyl)-pentanal, thymyl isovalerate, and p-cymen-8-yl demonstrated a favorable chemical correlation with DEET and also had high-affinity interactions with the OBP binding pocket that molecular dynamics simulations showed to be stable. To the best of our knowledge, this is the first study to evaluate on a large scale the potentiality of NPs from essential oils as inhibitors of the mosquito OBP1 using in silico approaches. Our results could facilitate the design of novel repellents with improved selectivity and affinity to the protein binding pocket and can shed light on the mechanism of action of these compounds against insect olfactory recognition.