The COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, and transmembrane protease serine 2 (TMPRSS2). In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat Mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of the drug candidates to target receptors, providing insight into their potential efficacy. This study is aimed at assessing the useful contributions of the application of computational instruments in the discovery of receptors targeted in SARS-CoV-2. It further highlights some identified advantages and limitations of these studies, thereby revealing some complementary experimental validation to ensure the efficacy and safety of identified drug candidates.

Hepatitis B virus (HBV) is an important global public health issue. The World Health Organization (WHO) 2024 Global Hepatitis Report estimated that the global prevalence of people living with HBV infection is 254 million, with an estimated prevalence incidence of 1.2 million new HBV infections yearly. Previous studies have shown that natural compounds have antiviral inhibition potentials. In silico methods such as molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR), and molecular dynamic simulations have been successfully applied in identifying bioactive compounds with strong binding energies in HBV treatment targets. The COVID-19 pandemic necessitated the importance of repurposing already approved drugs using in silico methods. This study is aimed at unveiling the benefits of in silico techniques as a potential alternative in natural compounds’ drug discovery and repurposing for HBV therapy. Relevant articles from PubMed, Google Scholar, and Web of Science were retrieved and analyzed. Furthermore, this study comprehensively reviewed the literature containing identified bioactive compounds with strong inhibition of essential HBV proteins. Notably, hesperidin, quercetin, kaempferol, myricetin, and flavonoids have shown strong binding energies for hepatitis B surface antigen (HBsAg). The investigation reveals that in silico drug discovery methods offer an understanding of the mechanisms of action, reveal previously overlooked viral targets (including PreS1 Domain of HBsAg and cccDNA (Covalently Closed Circular DNA) regulators, and facilitate the creation of specific inhibitors. The integration of in silico, in vitro, and in vivo techniques is essential for the discovery of new drugs for HBV therapy. The insights further highlight the importance of natural compounds and in silico methods as targets in drug discovery for HBV therapy. Moreover, the combination of natural compounds, an in silico approach, and drug repurposing improves the chances of personalized and precision medicine in HBV treatment. Therefore, we recommend drug repurposing strategies that combine in vitro, in vivo, and in silico approaches to facilitate the discovery of effective HBV drugs.

In over a century since its discovery, Alzheimer’s disease (AD) has continued to be a global health concern due to its incurable nature and overwhelming increase among older people. In this paper, we give an overview of the efforts of researchers towards identifying potent BACE1 exosite-binding antibodies and allosteric inhibitors. Herein, we apply computer-aided drug design (CADD) methods to unravel the interactions of some proposed psychotic and meroterpenoid BACE1 allosteric site inhibitors. This study is aimed at validating the allosteric potentials of these selected compounds targeted at BACE1 inhibition. Molecular docking, molecular dynamic (MD) simulations, and post-MD analyses are carried out on these selected compounds, which have been experimentally proven to exhibit allosteric inhibition on BACE1. The SwissDock software enabled us to identify more than five druggable pockets on the BACE1 structural surface using docking. Besides the active site region, a melatonin derivative (compound 1) previously proposed as a BACE1 allostery inhibitor showed appreciable stability at eight different subsites on BACE1. Refinement with molecular dynamic (MD) simulations shows that the identified non-catalytic sites are potential allostery sites for compound 1. The allostery and binding mechanism of the selected potent inhibitors show that the smaller the molecule, the easier the attachment to several enzyme regions. This finding hereby establishes that most of these selected compounds failed to exhibit strong allosteric binding with BACE1 except for compound 1. We hereby suggest that further studies and additional identification/validation of other BACE1 allosteric compounds be done. Furthermore, this additional allosteric site investigation will help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric BACE1 inhibitors.

The COVID-19 pandemic, instigated by the emergence of the novel coronavirus, SARS-CoV-2, created an incomparable global health crisis. Due to its highly virulent nature, identifying potential therapeutic agents against this lethal virus is crucial. PLpro is a key protein involved in viral polyprotein processing and immune system evasion, making it a prime target for the development of antiviral drugs to combat COVID-19. To expedite the search for potential therapeutic candidates, this review delved into computational studies. Recent investigations have harnessed computational methods to identify promising inhibitors targeting PLpro, aiming to suppress the viral activity. Molecular docking techniques were employed by researchers to explore the binding sites for antiviral drugs within the catalytic region of PLpro. The review elucidates the functional and structural properties of SARS-CoV-2 PLpro, underscoring its significance in viral pathogenicity and replication. Through comprehensive all-atom molecular dynamics (MD) simulations, the stability of drug–PLpro complexes was assessed, providing dynamic insights into their interactions. By evaluating binding energy estimates from MD simulations, stable drug–PLpro complexes with potential antiviral properties were identified. This review offers a comprehensive overview of the potential drug/lead candidates discovered thus far against PLpro using diverse in silico methodologies, encompassing drug repurposing, structure-based, and ligand-based virtual screenings. Additionally, the identified drugs are listed based on their chemical structures and meticulously examined according to various structural parameters, such as the estimated binding free energy (ΔG), types of intermolecular interactions, and structural stability of PLpro–ligand complexes, as determined from the outcomes of the MD simulations. Underscoring the pivotal role of targeting SARS-CoV-2 PLpro in the battle against COVID-19, this review establishes a robust foundation for identifying promising antiviral drug candidates by integrating molecular dynamics simulations, structural modeling, and computational insights. The continual imperative for the improvement of existing drugs and exploring novel compounds remains paramount in the global efforts to combat COVID-19. The evolution and management of COVID-19 hinge on the symbiotic relationship between computational insights and experimental validation, underscoring the interdisciplinary synergy crucial to this endeavor.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for COVID-19, which was declared a global pandemic in March 2020 by the World Health Organization (WHO). Since SARS-CoV-2 main protease plays an essential role in the virus’s life cycle, the design of small drug molecules with lower molecular weight has been a promising development targeting its inhibition. Herein, we evaluated the novel peptidomimetic azatripeptide and azatetrapeptide nitriles against SARS-CoV-2 main protease. We employed molecular dynamics (MD) simulations to elucidate the selected compounds’ binding free energy profiles against SARS-CoV-2 and further unveil the residues responsible for the drug-binding properties. Compound 8 exhibited the highest binding free energy of −49.37 ± 0.15 kcal/mol, followed by compound 7 (−39.83 ± 0.19 kcal/mol), while compound 17 showed the lowest binding free energy (−23.54 ± 0.19 kcal/mol). In addition, the absorption, distribution, metabolism, and excretion (ADME) assessment was performed and revealed that only compound 17 met the drug-likeness parameters and exhibited high pharmacokinetics to inhibit CYP1A2, CYP2C19, and CYP2C9 with better absorption potential and blood-brain barrier permeability (BBB) index. The additional intermolecular evaluations suggested compound 8 as a promising drug candidate for inhibiting SARS-CoV-2 Mpro. The substitution of isopropane in compound 7 with an aromatic benzene ring in compound 8 significantly enhanced the drug’s ability to bind better at the active site of the SARS-CoV-2 Mpro.

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has sparked intense research on its spike protein, which is essential for viral entrance into host cells. Viral reproduction and transmission, host immune response regulation, receptor recognition and host cell entrance mechanisms, as well as structural and functional effects have all been linked to mutations in the spike protein. Spike protein mutations can also result in immune evasion mechanisms that impair vaccine effectiveness and escape, and they are linked to illness severity and clinical consequences. Numerous studies have been conducted to determine the effects of these mutations on the spike protein structure and how it interacts with host factors. These results have important implications for the design and development of medicines and vaccines based on spike proteins as well as for the assessment of those products’ efficiency against newly discovered spike protein mutations. This paper gives a general overview of how spike protein mutations are categorized and named. It further looks at the links between spike protein mutations and clinical outcomes, illness severity, unanswered problems, and future research prospects. Additionally, explored are the effects of these mutations on vaccine effectiveness as well as the possible therapeutic targeting of spike protein mutations.