The current emergency due to the worldwide spread of the COVID-19 caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a great concern for global public health. Already in the past, the outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle Eastern respiratory syndrome (MERS) in 2012 demonstrates the potential of coronaviruses to cross-species borders and further underlines the importance of identifying new-targeted drugs. An ideal antiviral agent should target essential proteins involved in the lifecycle of SARS-CoV. Currently, some HIV protease inhibitors (i.e., Lopinavir) are proposed for the treatment of COVID-19, although their effectiveness has not yet been assessed. The main protease (M^pro) provides a highly validated pharmacological target for the discovery and design of inhibitors. We identified potent M^pro inhibitors employing computational techniques that entail the screening of a Marine Natural Product (MNP) library. MNP library was screened by a hyphenated pharmacophore model, and molecular docking approaches. Molecular dynamics and re-docking further confirmed the results obtained by structure-based techniques and allowed this study to highlight some crucial aspects. Seventeen potential SARS-CoV-2 M^pro inhibitors have been identified among the natural substances of marine origin. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds could be bioactive is excellent.

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.

Botulinum toxins are neurotoxins produced by Clostridium botulinum. This toxin can be lethal for humans as a cause of botulism; however, in small doses, the same toxin is used to treat different conditions. Even if the therapeutic doses are effective and safe, the adverse reactions could be local and could unmask a subclinical impairment of neuromuscular transmissions. There are not many cases of adverse events in the literature; however, it is possible that sometimes they do not occur as they are transient and, if they do occur, there is no possibility of a cure other than to wait for the pharmacological effect to end. Inhibition of botulinum neurotoxin type A (BoNT/A) effects is a strategy for treating botulism as it can provide an effective post-exposure remedy. In this paper, 13,592,287 compounds were screened through a pharmacophore filter, a 3D-QSAR model, and a virtual screening; then, the compounds with the best affinity were selected. Molecular dynamics simulation studies on the first four compounds predicted to be the most active were conducted to verify that the poses foreseen by the docking were stable. This approach allowed us to identify compounds with a calculated inhibitory activity in the range of 316–500 nM.

Herein we describe, for the first time, the covalent immobilization of catalase (CAT) on functionalized graphene surfaces (G) by exploiting the azalactone chemistry for the post-functionalization of graphene-based materials. The structure, morphology and chemical composition of catalase immobilized on graphene (CAT-G) have been investigated by Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX). The biological responses such as catalytic activity, cellular uptake, internalization pathway, and the ability to protect lymphocytes from oxidative stress induced by H₂O₂ together with the unforeseen ability to increase the lifetime of the free catalase in solution have been deeply investigated. From our studies, it is evident that the behavior of CAT covalently linked to modified graphene depends on the CAT/G ratio that affects the secondary structure and the tetramer stability of CAT. In order to support the experimental results, we have also investigated the behaviors of two appropriately designed model systems, named CAT-surfer and CAT-skier, by molecular dynamics calculations. These in silico results parallel the experimental results proving our hypothesis that the CAT-surfer maintains the conformational flexibility needed for a biological response, whereas CAT-skier favors the dissociation of the tetramer subunits, involving the inactivation of the enzyme.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a severe global health crisis. In this paper, we used docking and simulation methods to identify potential targets and the mechanism of action of chloroquine (CQ) and hydroxychloroquine (HCQ) against SARS-CoV-2. Our results showed that both CQ and HCQ influenced the functionality of the envelope (E) protein, necessary in the maturation processes of the virus, due to interactions that modify the flexibility of the protein structure. Furthermore, CQ and HCQ also influenced the proofreading and capping of viral RNA in SARS-CoV-2, performed by nsp10/nsp14 and nsp10/nsp16. In particular, HCQ demonstrated a better energy binding with the examined targets compared to CQ, probably due to the hydrogen bonding of the hydroxyl group of HCQ with polar amino acid residues.

Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. In particular, peculiar biological and therapeutic functions are associated with the sigma-2 (σ₂) receptor. The σ₂ receptor ligands determine tumor cell death through apoptotic and non-apoptotic pathways, and the overexpression of σ₂ receptors in several tumor cell lines has been well documented, with significantly higher levels in proliferating tumor cells compared to quiescent ones. This acknowledged feature has found practical application in the development of cancer cell tracers and for ligand-targeting therapy. In this context, the development of new ligands that target the σ₂ receptors is beneficial for those diseases in which this protein is involved. In this paper, we conducted a search of new potential σ₂ receptor ligands among a database of 1517 "small" marine natural products constructed by the union of the Seaweed Metabolite and the Chemical Entities of Biological Interest (ChEBI) Databases. The structures were passed through two filters that were constituted by our developed two-dimensional (2D) and three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) statistical models, and successively docked upon a σ₂ receptor homology model that we built according to the FASTA sequence of the σ₂/TMEM97 (SGMR2_HUMAN) receptor.