Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Background and Purpose: Tuberculosis has been reported to be the worldwide leading cause of death resulting from a sole infectious agent. The emergence of multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis has made the battle against the infection more difficult since most currently available therapeutic options are ineffective against these resistant strains. Therefore, novel molecules need to be developed to effectively treat tuberculosis disease. Preliminary docking studies revealed that tetrahydropyrimidinone derivatives have favorable interactions with the thymidylate kinase receptor. In the present investigation, we report the synthesis and the mycobacterial activity of several pyrimidinones and pyrimidinethiones as potential thymidylate kinase inhibitors. Methods: The title compounds ( 1a–d) and ( 2a–b) were synthesized by a one-pot three-component Biginelli reaction. They were subsequently characterized and used for whole-cell anti-TB screening against H37Rv and multidrug-resistant (MDR) strains of Mycobacterium tuberculosis (MTB) by the resazurin microplate assay (REMA) plate method. Molecular modeling was conducted using the Accelry’s Discovery Studio 4.0 client program to explain the observed bioactivity of the compounds. The pharmacokinetic properties of the synthesized compounds were predicted and analyzed. Results: Of the compounds tested for anti-TB activity, pyrimidinone 1a and pyrimidinethione 2a displayed moderate activity against susceptible MTB H37Rv strains at 16 and 32 μg/mL, respectively. Only compound 2a was observed to exert modest activity at 128 μg/mL against MTB strains with cross-resistance to rifampicin and isoniazid. The presence of the trifluoromethyl group was essential to retain the inhibitory activity of compounds 1a and 2a. Molecular modeling studies of these compounds against thymidylate kinase targets demonstrated a positive correlation between the bioactivity and structure of the compounds. The in-silico ADME (absorption, distribution, metabolism, and excretion) prediction indicated favorable pharmacokinetic and drug-like properties for most compounds. Conclusion: Pyrimidinone 1a and pyrimidinethione 2a were identified as the leading compounds and can serve as a starting point to develop novel anti-TB therapeutic agents.

As a member of the Orthomyxoviridae family of viruses, influenza viruses (IVs) are known causative agents of respiratory infection in vertebrates. They remain a major global threat responsible for the most virulent diseases and global pandemics in humans. The virulence of IVs and the consequential high morbidity and mortality of IV infections are primarily attributed to the high mutation rates in the IVs’ genome coupled with the numerous genomic segments, which give rise to antiviral resistant and vaccine evading strains. Current therapeutic options include vaccines and small molecule inhibitors, which therapeutically target various catalytic processes in IVs. However, the periodic emergence of new IV strains necessitates the continuous development of novel anti-influenza therapeutic options. The crux of this review highlights the recent studies on the biology of influenza viruses, focusing on the structure, function, and mechanism of action of the M2 channel and neuraminidase as therapeutic targets. We further provide an update on the development of new M2 channel and neuraminidase inhibitors as an alternative to existing anti-influenza therapy. We conclude by highlighting therapeutic strategies that could be explored further towards the design of novel anti-influenza inhibitors with the ability to inhibit resistant strains.

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.

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell’s life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.