Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.

Natural products and plant extracts exhibit many biological activities, including that related to the defense mechanisms against parasites. Many studies have investigated the biological functions of secondary metabolites and reported evidence of antiviral activities. The pandemic emergencies have further increased the interest in finding antiviral agents, and efforts are oriented to investigate possible activities of secondary plant metabolites against human viruses and their potential application in treating or preventing SARS-CoV-2 infection. In this review, we performed a comprehensive analysis of studies through in silico and in vitro investigations, also including in vivo applications and clinical trials, to evaluate the state of knowledge on the antiviral activities of secondary metabolites against human viruses and their potential application in treating or preventing SARS-CoV-2 infection, with a particular focus on natural compounds present in food plants. Although some of the food plant secondary metabolites seem to be useful in the prevention and as a possible therapeutic management against SARS-CoV-2, up to now, no molecules can be used as a potential treatment for COVID-19; however, more research is needed.

Volatile organic compounds (VOCs) from ‘emitting’ plants inform the ‘receiving’ (listening) plants of impending stresses or simply of their presence. However, the receptors that allow receivers to detect the volatile cue are elusive. Most likely, plants (as animals) have odorant-binding proteins (OBPs), and in fact, a few OBPs are known to bind ‘stress-induced’ plant VOCs. We investigated whether these and other putative OBPs may bind volatile constitutive and stress-induced isoprenoids, the most emitted plant VOCs, with well-established roles in plant communication and defense. Molecular docking simulation experiments suggest that structural features of a few plant proteins screened in databases could allow VOC binding. In particular, our results show that monoterpenes may bind the same plant proteins that were described to bind other stress-induced VOCs, while the constitutive hemiterpene isoprene is unlikely to bind any investigated putative OBP and may not have an info-chemical role. We conclude that, as for animal, there may be plant OBPs that bind multiple VOCs. Plant OBPs may play an important role in allowing plants to eavesdrop messages by neighboring plants, triggering defensive responses and communication with other organisms.

An outbreak by a new severe acute respiratory syndrome betacoronavirus (SARS-CoV-2) has spread CoronaVirus Disease 2019 (COVID-19) all over the world. Immediately, following studies have confirmed the human Angiotensin-Converting Enzyme 2 (ACE2) as a cellular receptor of viral Spike-Protein (Sp) that mediates the CoV-2 invasion into the pulmonary host cells. Here, we compared the molecular interactions of the viral Sp from previous SARS-CoV-1 of 2002 and SARS-CoV-2 with the host ACE2 protein by in silico analysis of the available experimental structures of Sp-ACE2 complexes. The K417 amino acid residue, located in the region of Sp Receptor-Binding Domain (RBD) of the new coronavirus SARS-CoV-2, showed to have a key role for the binding to the ACE2 N-terminal region. The R426 residue of SARS-CoV-1 Sp-RBD also plays a key role, although by interacting with the central region of the ACE2 sequence. Therefore, our study evidenced peculiarities in the interactions of the two Sp-ACE2 complexes. Our outcomes were consistent with previously reported mutagenesis studies on SARS-CoV-1 and support the idea that a new and different RBD was acquired by SARS-CoV-2. These results have interesting implications and suggest further investigations.

The affinity of specific phenolic compounds (PCs) and capsaicinoids (CAPs) present in three Capsicum annuum varieties (Friariello, Cayenne and Dzuljunska Sipka) to the transient receptor potential vanilloid member 1 (TRPV1) was investigated by integrating an analytic approach for the simultaneous extraction and analysis through high-performance liquid chromatography coupled with ion trap mass spectrometry (HPLC/ITMS) and UV detection (HPLC-UV) of PCs and CAPs and structural bioinformatics based on the protein modelling and molecular simulations of protein–ligand docking. Overall, a total of 35 compounds were identified in the different samples and CAPs were quantified. The highest content of total polyphenols was recorded in the pungent Dzuljunska Sipka variety (8.91 ± 0.05 gGAE/Kg DW) while the lowest was found in the non-pungent variety Friariello (3.58 ± 0.02 gGAE/Kg DW). Protein modelling generated for the first time a complete model of the homotetrameric human TRPV1, and it was used for docking simulations with the compounds detected via the analytic approach, as well as with other compounds, as an inhibitor reference. The simulations indicate that different capsaicinoids can interact with the receptor, providing details on the molecular interaction, with similar predicted binding energy values. These results offer new insights into the interaction of capsaicinoids with TRPV1 and their possible actions.