Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Optimization, and biological evaluation of 3-O-ß-chacotriosyl betulinic acid amide derivatives as novel small-molecule Omicron.

SARS-CoV-2 Omicron viruses possess a high antigenic shift, and the approved anti-SARS-CoV-2 drugs are extremely limited, which makes the development of new antiviral drugs for the clinical treatment and prevention of SARS-CoV-2 outbreaks imperative. We have previously discovered a new series of markedly potent small-molecule inhibitors of SARS-CoV-2 virus entry, exampled by the hit compound 2. Here, we report a further study of bioisosteric replacement of the eater linker at the C-17 position of 2 with a variety of aromatic amine moieties, followed by a focused structure-activity relationship study, leading to the discovery of a series of novel 3-O-ß-chacotriosyl BA amide derivatives as small-molecule Omicron fusion inhibitors with improved potency and selectivity index. Particularly, our medicinal chemistry efforts have resulted in a potent, and efficacious lead compound S-10 with appreciable pharmacokinetic properties, which exhibited broad-spectrum potency against Omicron and other variants with EC50 values ranging from 0.82 to 5.45 µM. Mutagenesis studies confirmed that inhibition of Omicron viral entry was mediated by the direct interaction with S in the prefusion state. These results reveal that S-10 is suitable for further optimization as Omicron fusion inhibitors, with the potential to be developed as therapeutic agents for the treatment and control of SARS-CoV-2 ant its variants infections.

2d Qsar Approach to Develop Newer Generation Small Molecules Active against Sars-Covid

We are in the half past of 2022, but still, we are facing the coronavirus pandemic situation. When a patient is hospitalized, only some FDA-approved drugs were administered to cure the patient. In treating coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors, and various monoclonal antibodies are present. But all the molecules used in the treatment were not so effective in fully curing the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches played an essential role. 2D QSAR studies related to anti-SARS-CoV-2 molecule development, some QSAR models observed with good statistical parameters such as R2: 0.748, cross-validated Q2 (LOO): 0.628, external predicted R2: 0.723 and another model suggested with R2: 0.764, Q2: 0.627 and Rm2: 0.610, Q2 (F1): 0.727, Q2 (F1): 0.652, MAE score: 0.127. We developed a new 2D QSAR model with a higher number of molecules and greater statistical parameters. A dataset of 84 anti-SARS-CoV2 molecules was obtained from literature followed by descriptor calculation PADEL software;the QSAR model was generated using the Modelability index, dataset pretreatment, division, MLR equation, validation, and Y randomization test. The model was pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP with R2 and Q2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298. Atoms E-state and electronic features of the molecules directly related to anti-SARS-CoV-2 drug activity. It can be easily concluded that we want to develop a small molecule effective against SARS-CoV-2 disease in the near future.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.

Predictive analysis, diagnosis of COVID-19 through computational screening and validation with spectro photometrical approach

Objective: To develop Favipiravir, based predictive models of coronavirus disease 2019 (COVID-19) from small molecule databases such as PubChem, Drug Bank, Zinc Database, and literature. Method(s): High Throughput Virtual Screening (HTVS) using different computational screening methods is used to identify the target and lead molecules. CoMFA (Comparative Molecular Field Analysis) is a 3D-QSAR procedure depending on information from known dynamic atoms and eventually permits one to plan and anticipate exercises of particles. These two analysis is used to train predictive models. Result(s): The predictive model achieved the highest accuracy score with a relatively small dataset size can be a subject of overfitting. Datasets with over 500 samples demonstrate an accuracy of about 85-95%, that can be considered as very good. Conclusion(s): From the result it is observed that Increasing level of potassium, sodium and nitrogen will lead to burst lipid bilayer membrane of virus which cause RNA replication rapidly. However, low level of sodium, potassium and nitrogen will help in the DNA polymerase inhibition and replication can be stopped. The best developed QSAR model in terms of the druggability and activity relation has been selected over the parent Favipiravir molecule for designing COVID-19 drugs may lead towards pharmaceutical development in future.Copyright © 2023, The Author(s), under exclusive licence to Korean Society of Environmental Risk Assessment and Health Science.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Remdesivir analogs against SARS-CoV-2 RNA-dependent RNA polymerase.

The COVID-19 pandemic has already taken many lives but is still continuing its spread and exerting jeopardizing effects. This study is aimed to find the most potent ligands from 703 analogs of remdesivir against RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus . RdRp is a major part of a multi-subunit transcription complex of the virus, which is essential for viral replication. In clinical trials, it has been found that remdesivir is effective to inhibit viral replication in Ebola and in primary human lung cell cultures; it effectively impedes replication of a broad-spectrum pre-pandemic bat coronaviruses and epidemic human coronaviruses. After virtual screening, 30 most potent ligands and remdesivir were modified with triphosphate. Quantum mechanics-based quantitative structure-activity relationship envisages the binding energy for ligands applying partial least square (PLS) regression. PLS regression remarkably predicts the binding energy of the effective ligands with an accuracy of 80% compared to the value attained from molecular docking. Two ligands (L4:58059550 and L28:126719083), which have more interactions with the target protein than the other ligands including standard remdesivir triphosphate, were selected for further analysis. Molecular dynamics simulation is done to assess the stability and dynamic nature of the drug-protein complex. Binding-free energy results via PRODIGY server and molecular mechanics/Poisson-Boltzmann surface area method depict that the potential and solvation energies play a crucial role. Considering all computational analysis, we recommend the best remdesivir analogs can be utilized for efficacy test through in vitro and in vivo trials against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Anti-Entry Activity of Natural Flavonoids against SARS-CoV-2 by Targeting Spike RBD.

COVID-19 is still a global public health concern, and the SARS-CoV-2 mutations require more effective antiviral agents. In this study, the antiviral entry activity of thirty-one flavonoids was systematically evaluated by a SARS-CoV-2 pseudovirus model. Twenty-four flavonoids exhibited antiviral entry activity with IC50 values ranging from 10.27 to 172.63 µM and SI values ranging from 2.33 to 48.69. The structure-activity relationship of these flavonoids as SARS-CoV-2 entry inhibitors was comprehensively summarized. A subsequent biolayer interferometry assay indicated that flavonoids bind to viral spike RBD to block viral interaction with ACE2 receptor, and a molecular docking study also revealed that flavonols could bind to Pocket 3, the non-mutant regions of SARS-CoV-2 variants, suggesting that flavonols might be also active against virus variants. These natural flavonoids showed very low cytotoxic effects on human normal cell lines. Our findings suggested that natural flavonoids might be potential antiviral entry agents against SARS-CoV-2 via inactivating the viral spike. It is hoped that our study will provide some encouraging evidence for the use of natural flavonoids as disinfectants to prevent viral infections.

An Updated and Focused Review on Heterocyclic Inhibitors for SARS-CoV and SARS-CoV-2 3CLpro.

BACKGROUND: SARS-CoV and SARS-CoV-2 are extremely infective and frequently cause severe respiratory issues (acute respiratory syndrome). These emerging viruses represent huge challenges to worldwide health. Reliable and systematic examination of SARS-CoV and COVID-19 will assist in identifying infectious persons accurately. Based on the biological, chemical, and genetic link of SARS CoV-2 towards SARS-CoV, the recurrence of different anti-SARS-CoV natural drug molecules may be beneficial in the advancement of anti COVID-19 herbal drug molecules. Here, we reviewed published literature on SAR, and molecular docking study of previously synthesized derivatives, which targeted SARS-CoV along with future prospects on SARS-CoV-2 have been reviewed. This study would assist researchers in developing newer/novel potential molecules that would target SAR-CoV-2. OBJECTIVES: The review highlights the inhibition potential of heterocyclic inhibitors for SARS-CoV and SARS-CoV-2. The structure-activity relationship of potential compounds has been discussed. EVIDENCE ACQUISITION: We carried out a thorough literature assessment employing electronic databases for scientific articles highlighting potential heterocyclic inhibitors for SARS-CoV and SARS-CoV-2, published from 2010 to 2021. We recovered 415 articles, but only 220 were involved and conversed in this manuscript. The article apprehended appropriate research considering three areas: 1) SAR activity, 2) Molecular docking, and 3) Biological activity and future prospects on SARS-CoV-2. METHODS: The potential compounds with good inhibition are discussed, and their inhibition is expressed in terms of IC50. RESULTS: Heterocyclic scaffolds reflected an extensive range of therapeutic activity and may act as an initiating idea for designing and discovering potential inhibitors for SARS-CoV and SARS-CoV-2 treatment. CONCLUSION: The points highlighted here may prove to be a vital tool for medicinal chemists working/investigating more potent and efficacious scaffolds in treating SARS-CoV and SARS-CoV-2.

COVID-19 Drug Discovery and Comparison Between Generative and Predictive Approach

The spread of the SARS-Cov-2 virus in the human race has caused 6.56M deaths worldwide as of Oct 2022 and has brought the economy to a standstill. It has also introduced several challenges worldwide. So, the need of finding a drug that would be able to dilute the symptoms of COVID-19 in the patients. The current methods for drug discovery via conventional methods are a tedious and time-consuming process. So here, the deep learning algorithms come to our rescue. Scientists and Doctors are diligently studying and analyzing the genome sequence of the virus and trying to understand the interaction between the coronavirus protease and a covalent inhibitor. Taking advantage of one such research work published by Shanghai Tech University, the research attempts in making research which is based on an approach to inhibit the protease of SARS-Cov-2(Or any virus) by a covalent inhibitor(also called Ligand). The research was done for some similar viruses to SARS-Cov-2, like SARS, MERS, and HIV. Protein target GI73745819 - SARS Protease, Protein target GI75593047 - HIV pol polyprotein, NS3 - Hep3 protease, and 3CL-Pro - Mers Protease. Bioactivities measured in these papers by medicinal chemists and biochemists are tracked by The National Center for Biotechnology Information (NCBI) which can be accessed by everyone. The goal of this research is to make efforts toward proposing a potentially highly active molecule against a target protein of the 2019 Novel Coronavirus. This research features training of the model in such a way that it predicts the binding power of the drug toward COVID-19 protease. Then compared and reported the inhibition score of ligand and protease to find out one of the best inhibitors. © 2022 IEEE.

Molecular modelling and structure-activity relationship of a natural derivative of o-hydroxybenzoate as a potent inhibitor of dual NSP3 and NSP12 of SARS-CoV-2: in silico study.

The nsp3 macrodomain and nsp12 (RdRp) enzymes are strongly implicated in the virulent regulation of the host immune response and viral replication of SARS-CoV-2, making them plausible therapeutic targets for mitigating infectivity. Remdesivir remains the only FDA-approved small-molecule inhibitor of the nsp12 in clinical conditions while none has been approved yet for the nsp3 macrodomain. In this study, 69,067 natural compounds from the IBScreen database were screened for efficacious potentials with mechanistic multitarget-directed inhibitory pharmacology against the dual targets using in silico approaches. Standard and extra precision (SP and XP) Maestro glide docking analyses were employed to evaluate their inhibitory interactions against the enzymes. Four compounds, STOCK1N-45901, 03804, 83408, 08377 consistently showed high XP scores against the respective targets and interacted strongly with pharmacologically essential amino acid and RNA residues, in better terms than the standard, co-crystallized inhibitors, GS-441524 and remdesivir. Further assessments through the predictions of ADMET and mutagenicity distinguished STOCK1N-45901, a natural derivative of o-hydroxybenzoate as the most promising candidate. The ligand maintained a good conformational and thermodynamic stability in complex with the enzymes throughout the trajectories of 100 ns molecular dynamics, indicated by RMSD, RMSF and radius of gyration plots. Its binding free energy, MM-GBSA was recorded as -54.24 and -31.77 kcal/mol against the respective enzyme, while its structure-activity relationships confer high probabilities as active antiviral, anti-inflammatory, antiinfection, antitussive and peroxidase inhibitor. The IBScreen database natural product, STOCK1N-45901 (2,3,4,5,6-pentahydroxyhexyl o-hydroxybenzoate) is thus recommended as a potent inhibitor of dual nsp3 and nsp12 of SARS-CoV-2 for further study. Communicated by Ramaswamy H. Sarma.

Total synthesis of antiviral drug, nirmatrelvir (PF-07321332).

The emergence and rapid spread of coronavirus disease 2019 (COVID-19), a potentially fatal disease, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has swiftly led to public health crisis worldwide. Hence vaccines and antiviral therapeutics are an important part of the healthcare response to combat the ongoing threat by COVID-19. Here, we report an efficient synthesis of nirmatrelvir (PF-07321332), an orally active SARS-CoV-2 main protease inhibitor.

Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells.

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances.

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.

Promising Marine Natural Products for Tackling Viral Outbreaks: A Focus on Possible Targets and Structure-Activity Relationship.

Recently, people worldwide have experienced several outbreaks caused by viruses that have attracted much interest globally, such as HIV, Zika, Ebola, and the one being faced, SARSCoV-2 viruses. Unfortunately, the availability of drugs giving satisfying outcomes in curing those diseases is limited. Therefore, it is necessary to dig deeper to provide compounds that can tackle the causative viruses. Meanwhile, the efforts to explore marine natural products have been gaining great interest as the products have consistently shown several promising biological activities, including antiviral activity. This review summarizes some products extracted from marine organisms, such as seaweeds, seagrasses, sponges, and marine bacteria, reported in recent years to have potential antiviral activities tested through several methods. The mechanisms by which those compounds exert their antiviral effects are also described here, with several main mechanisms closely associated with the ability of the products to block the entry of the viruses into the host cells, inhibiting replication or transcription of the viral genetic material, and disturbing the assembly of viral components. In addition, the structure-activity relationship of the compounds is also highlighted by focusing on six groups of marine compounds, namely sulfated polysaccharides, phlorotannins, terpenoids, lectins, alkaloids, and flavonoids. In conclusion, due to their uniqueness compared to substances extracted from terrestrial sources, marine organisms provide abundant products having promising activities as antiviral agents that can be explored to tackle virus-caused outbreaks.

Inhibitors of SARS-CoV-2 PLpro.

The emergence of SARS-CoV-2 causing the COVID-19 pandemic, has highlighted how a combination of urgency, collaboration and building on existing research can enable rapid vaccine development to fight disease outbreaks. However, even countries with high vaccination rates still see surges in case numbers and high numbers of hospitalized patients. The development of antiviral treatments hence remains a top priority in preventing hospitalization and death of COVID-19 patients, and eventually bringing an end to the SARS-CoV-2 pandemic. The SARS-CoV-2 proteome contains several essential enzymatic activities embedded within its non-structural proteins (nsps). We here focus on nsp3, that harbours an essential papain-like protease (PLpro) domain responsible for cleaving the viral polyprotein as part of viral processing. Moreover, nsp3/PLpro also cleaves ubiquitin and ISG15 modifications within the host cell, derailing innate immune responses. Small molecule inhibition of the PLpro protease domain significantly reduces viral loads in SARS-CoV-2 infection models, suggesting that PLpro is an excellent drug target for next generation antivirals. In this review we discuss the conserved structure and function of PLpro and the ongoing efforts to design small molecule PLpro inhibitors that exploit this knowledge. We first discuss the many drug repurposing attempts, concluding that it is unlikely that PLpro-targeting drugs already exist. We next discuss the wealth of structural information on SARS-CoV-2 PLpro inhibition, for which there are now ∼30 distinct crystal structures with small molecule inhibitors bound in a surprising number of distinct crystallographic settings. We focus on optimisation of an existing compound class, based on SARS-CoV PLpro inhibitor GRL-0617, and recapitulate how new GRL-0617 derivatives exploit different features of PLpro, to overcome some compound liabilities.

A comprehensive evaluation of the potential binding poses of fentanyl and its analogs at the µ-opioid receptor.

Fentanyl and its analogs are selective agonists of the µ-opioid receptor (MOR). Among novel synthetic opioids (NSOs), they dominate the recreational drug market and are the main culprits for the opioid crisis, which has been exacerbated by the COVID-19 pandemic. By taking advantage of the crystal structures of the MOR, several groups have investigated the binding mechanism of fentanyl, but have not reached a consensus, in terms of both the binding orientation and the fentanyl conformation. Thus, the binding mechanism of fentanyl at the MOR remains an unsolved and challenging question. Here, we carried out a systematic computational study to investigate the preferred fentanyl conformations, and how these conformations are being accommodated in the MOR binding pocket. We characterized the free energy landscape of fentanyl conformations with metadynamics simulations, and compared and evaluated several possible fentanyl binding conditions in the MOR with long-timescale molecular dynamics simulations. Our results indicate that the most preferred binding pose in the MOR binding pocket corresponds well with the global minimum on the energy landscape of fentanyl in the absence of the receptor, while the energy landscape can be reconfigured by modifying the fentanyl scaffold. The interactions with the receptor may stabilize a slightly unfavored fentanyl conformation in an alternative binding pose. By extending similar investigations to fentanyl analogs, our findings establish a structure-activity relationship of fentanyl binding at the MOR. In addition to providing a structural basis to understand the potential toxicity of the emerging NSOs, such insights will contribute to developing new, safer analgesics.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection.

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure-activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01975-3.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

1,3,4-oxadiazole derivatives as potential antimicrobial agents.

Due to the emergence of drug-resistant microbial strains, different research groups are continuously developing novel drug molecules against already exploited and unexploited targets. 1,3,4-Oxadiazole derivatives exhibited noteworthy antimicrobial activities. The presence of 1,3,4-oxadiazole moiety in antimicrobial agents can modify their polarity and flexibility, which significantly improves biological activities due to various bonded and non-bonded interactions viz. hydrogen bond, steric, electrostatic, and hydrophobic with target sites. The present review elaborates the therapeutic targets and mode of interaction of 1,3,4-oxadiazoles as antimicrobial agents. 1,3,4-oxadiazole derivatives target enoyl reductase (InhA), 14α-demethylase in the mycobacterial cell; GlcN-6-P synthase, thymidylate synthase, peptide deformylase, RNA polymerase, dehydrosqualene synthase in bacterial strains; ergosterol biosynthesis pathway, P450-14α demethylase, protein-N-myristoyltransferase in fungal strains; FtsZ protein, interfere with purine and functional protein synthesis in plant bacteria. The present review also summarizes the effect of different moieties and functional groups on the antimicrobial activity of 1,3,4-oxadiazole derivatives.

Comparative Study of the Synthetic Approaches and Biological Activities of the Bioisosteres of 1,3,4-Oxadiazoles and 1,3,4-Thiadiazoles over the Past Decade.

The bioisosteres of 1,3,4-oxadiazoles and 1,3,4-thiadiazoles are well-known pharmacophores for many medicinally important drugs. Throughout the past 10 years, 1,3,4-oxa-/thiadiazole nuclei have been very attractive to researchers for drug design, synthesis, and the study of their potential activity towards a variety of diseases, including microbial and viral infections, cancer, diabetes, pain, and inflammation. This work is an up-to-date comparative study that identifies the differences between 1,3,4-thiadiazoles and 1,3,4-oxadiazoles concerning their methods of synthesis from different classes of starting compounds under various reaction conditions, as well as their biological activities and structure-activity relationship.