By studying the literature about Tetracyclines (TCs), it becomes clearly evident that TCs are very dynamic molecules. In some cases, their structure-activity-relationship (SAR) are known, especially against bacteria, while against other targets, they are virtually unknown. In other diverse yields of research, such as neurology, oncology and virology the utility and activity of the tetracyclines are being discovered and are also emerging as new technological fronts. The first aim of this paper is classify the compounds already used in therapy and prepare the schematic structure in which include the next generation of TCs. The aim of this work is introduce a new framework for the classification of old and new TCs, using a medicinal chemistry approach to the structure of that drugs. A fully documented Structure-Activity-Relationship (SAR) is presented with the analysis data of antibacterial and nonantibacterial (antifungal, antiviral and anticancer) tetracyclines. Lipophilicity of functional groups and conformations interchangeably are determining rules in biological activities of TCs.
Tetracyclines are an old class of molecules that constitute a broad-spectrum antibiotics. Since the first member of tetracycline family were isolated, the clinical importance of these compounds as therapeutic and prophylactic agents against a wide range of infections has stimulated efforts to define their mode of action as inhibitors of bacterial reproduction. We used three SAR methodologies for the analysis of biological activity of a set of 104 tetracycline compounds. Our calculation were carried out using the semi-empirical Austin Method One (AM1) and Parametric Method 3 (PM3). Electronic I…
The COVID-19 pandemic triggered by SARS-CoV-2 is a worldwide health disaster. Main protease is an attractive drug target among coronaviruses, due to its vital role in processing the polyproteins that are translated from the viral RNA. There is presently no exact drug or treatment for this diseases caused by SARS-CoV-2. In the present study, we report the potential inhibitory activity of some FDA approved drugs against SARS-CoV-2 main protease by molecular docking study to investigate their binding affinity in protease active site. Docking studies revealed that drug Oseltamivir (anti-H1N1 drug)…
Since the epidemic began in November 2019, no viable medicine against SARS-CoV-2 has been discovered. The typical medication discovery strategy requires several years of rigorous research and development as well as a significant financial commitment, which is not feasible in the face of the current epidemic. Through molecular docking and dynamic simulation studies, we used the FDA-approved drug mezonavir against the most important viral targets, including spike (S) glycoprotein, Transmembrane serine protease 2 (TMPRSS2), RNA-dependent RNA polymerase (RdRp), Main protease (Mpro), human angioten…
There remains an urgent need to identify existing drugs that might be suitable for treating patients suffering from COVID-19 infection. Drugs rarely act at a single molecular target, with off target effects often being responsible for undesirable side effects and sometimes, beneficial synergy between targets for a specific illness. Off target activities have also led to blockbuster drugs in some cases, e.g. Viagra for erectile dysfunction and Minoxidil for male pattern hair loss. Drugs already in use or in clinical trials plus approved natural products constitute a rich resource for discovery …
The main reasons for the ongoing COVID-19 (coronavirus disease 2019) pandemic are the unavailability of recommended efficacious drugs or vaccines along with the human to human transmission nature of SARS-CoV-2 virus. So, there is urgent need to search appropriate therapeutic approach by repurposing approved drugs. In this communication, molecular docking analyses of two influenza antiviral drugs baloxavir acid (BXA) and baloxavir marboxil (BXM) were performed with the three therapeutic target proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), i.e., main protease (Mpro), …
Covid-19, a serious respiratory complications caused by SARS-CoV-2 has become one of the global threat to human healthcare system. The present study evaluated the possibility of plant originated approved 117 therapeutics against the main protease protein (MPP), RNA-dependent RNA polymerase (RdRp) and spike protein (S) of SARS-CoV-2 including drug surface analysis by using molecular docking through drug repurposing approaches. The molecular interaction study revealed that Rifampin (-16.3 kcal/mol) were topmost inhibitor of MPP where Azobechalcone were found most potent plant therapeutics for bl…
The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different possibilities (including herbal medicines having known therapeutic significance) have been explored by the scientists. The systematic scientific study (beginning with in silico study) of herbal medicines in particular and any drug in general is now possible as the structural components (proteins) of COVID-19 are already characterized. The main protease of COVID-19 virus is $\rm{M^{pro}}$ or $\rm{3CL^{pr…
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学研究所）
SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the…
For fast development of COVID-19, it is only feasible to use drugs (off label use) or approved natural products that are already registered or been assessed for safety in previous human trials. These agents can be quickly assessed in COVID-19 patients, as their safety and pharmacokinetics should already be well understood. Computational methods offer promise for rapidly screening such products for potential SARS-CoV-2 activity by predicting and ranking the affinities of these compounds for specific virus protein targets. The RNA-dependent RNA polymerase (RdRP) is a promising target for SARS-Co…
The emergence of COVID-19 has severely compromised the arsenal of antiviral and antibiotic drugs. Drug discovery is a multistep process with a high failure rate, high cost and it takes approximately 10-12 years for the development of new molecules into the clinical candidate. On the other side, drug repurposing also called old drugs for new uses, is an attractive alternative approach for a new application of marketed FDA approved or investigational drugs. In the current pandemic situation raised due to COVID-19, repurposing of existing FDA approved drugs are emerging as the first line of the t…
Global coronavirus disease pandemic (COVID-19) caused by newly identified SARS- CoV-2 coronavirus continues to claim the lives of thousands of people worldwide. The unavailability of specific medications to treat COVID-19 has led to drug repositioning efforts using various approaches, including computational analyses. Such analyses mostly rely on molecular docking and require the 3D structure of the target protein to be available. In this study, we utilized a set of machine learning algorithms and trained them on a dataset of RNA-dependent RNA polymerase (RdRp) inhibitors to run inference anal…
Inspired by recent work on anti-covid-19 drugs \cite{2} here we study the Quantitative-structure property relationships(QSPR) of phytochemicals screened against SARS-CoV-2 $3CL^{pro}$ with the help of topological indices like the first Zagreb index $M_{1}$, second Zagreb index $M_{2}$, Randi$\acute{c}$ index $R$, Balban index $J$ and sum-connectivity index $SCI(G)$. Our study has raveled that the sum-connectivity index $(SCI)$ and the first Zagreb index $(M_{1})$ are two important parameters to predict the molecular weight and the topological polar surface area of phytochemicals respectively.
Currently, there is no effective antiviral drugs nor vaccine for coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its high conservativeness and low similarity with human genes, SARS-CoV-2 main protease (M$^{\text{pro}}$) is one of the most favorable drug targets. However, the current understanding of the molecular mechanism of M$^{\text{pro}}$ inhibition is limited by the lack of reliable binding affinity ranking and prediction of existing structures of M$^{\text{pro}}$-inhibitor complexes. This work integrates mathematics and deep lea…
Coronaviruses are enveloped, non-segmented positive-sense RNA viruses that have the largest genome among RNA viruses. The genome contains a large replicase ORF encodes nonstructural proteins (NSPs), structural and accessory genes. NSP15 is a nidoviral RNA uridylate-specific endoribonuclease (NendoU) has C-terminal catalytic domain. The endoribonuclease activity of NSP15 interferes with the innate immune response of the host. Here, we screened Selleckchem Natural product database of compounds against the NSP15, Thymopentin and Oleuropein showed highest binding energies. The binding of these mol…
The Coronavirus Disease (COVID-19) pandemic caused by the SARS-coronavirus 2 (SARS-CoV-2) urgently calls for the design of drugs directed against this new virus. Given its essential role in proteolytic processing, the main protease Mpro has been identified as an attractive candidate for drugs against SARS-CoV-2 and similar coronaviruses. Recent high-throughput screening studies have identified a set of existing, small-molecule drugs as potent Mpro inhibitors. Amongst these, Ebselen (2-Phenyl-1,2-benzoselenazol-3-one), a glutathione peroxidase mimetic seleno-organic compound, is particularly at…
The potential of virtual reality (VR) to contribute to drug design and development has been recognised for many years. Hardware and software developments now mean that this potential is beginning to be realised, and VR methods are being actively used in this sphere. A recent advance is to use VR not only to visualise and interact with molecular structures, but also to interact with molecular dynamics simulations of 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful not only for flexible docking but also to examine binding processes and conformational changes. iMD-VR h…
University of Science and Technology of China（中国科学技术大学）；Microsoft Research（微软研究院）；Renmin University of China（中国人民大学）
Structure-based drug design is drawing growing attentions in computer-aided drug discovery. Compared with the virtual screening approach where a pre-defined library of compounds are computationally screened, de novo drug design based on the structure of a target protein can provide novel drug candidates. In this paper, we present a generative solution named TamGent (Target-aware molecule generator with Transformer) that can directly generate candidate drugs from scratch for a given target, overcoming the limits imposed by existing compound libraries. Following the Transformer framework (a stat…
2019-nCoV is a novel coronavirus was isolated and identified in 2019 in Wuhan, China. On 17th February and according to world health organization, a number of 71 429 confirmed cases worldwide, among them 2162 new cases recorded in the last 24 hours. There is no drug or vaccine for human and animal coronavirus. The inhibition of 3CL hydrolase enzyme provides a promising therapeutic principle for developing treatments against CoViD-19. The 3CLpro (Mpro) known for involving in counteracting the host innate immune response. This work presents the inhibitory effect of some natural compounds against…
Recent advances in molecular machine learning, especially deep neural networks such as Graph Neural Networks (GNNs) for predicting structure activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep neural networks are limited by the requirement of large amounts of training data. In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks. In this work, in contrast to the popular parameter-based transfer …
