Updates on drug designing approach through computational strategies: a review.

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

An extensive survey of technological applications in drug discovery and development, encompassing offline and online approaches, is presented in this review. The span of research issues that can be tackled using these advances is vast, opening new horizons for future innovations. The article is designed to incite further research investments into drug development procedures and bridge existing research voids by outlining multiple pharmaceutical products that resulted from employing systematic computational methodologies.

The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery.

Complex disorders, such as depression, remain a mystery for scientists. Although genetic factors are considered important for the prediction of one's vulnerability, it is hard to estimate the exact risk for a patient to develop depression, based only on one category of vulnerability criteria. Genetic factors also regulate drug metabolism, and when they are identified in a specific combination, may result in increased drug resistance. A proper understanding of the genetic basis of depression assists in the development of novel promising medications and effective disorder management schemes. This review aims to analyze the recent literature focusing on the correlation between specific genes and the occurrence of depression. Moreover, certain aspects targeting a high drug resistance identified among patients suffering from major depressive disorder were highlighted in this manuscript. An expected direction of future drug discovery campaigns was also discussed.

CADD Studies in the Discovery of Potential ARI (Aldose Reductase Inhibitors) Agents for the Treatment of Diabetic Complications.

The lack of currently available drugs for the treatment of diabetes complications has stimulated our interest in finding new Aldose Reductase inhibitors (ARIs) with more beneficial biological properties. One metabolic method by the use of aldose reductase inhibitors in the first step of the polyol pathway. to control excess glucose flux in diabetic tissues. Computer-aided drug discovery (CADD) plays a key role in finding and optimizing potential lead substances. AR inhibitors (ARI) have been widely discussed in the literature, for example, Epalrestat is currently the only ARI used to treat patients with diabetic neuropathy in Japan, India, and China. Inhibiting R in patients with severe to moderate diabetic autonomic neuropathy has a beneficial effect on heart rate variability. AT-001, an AR inhibitor, is now being tested in COVID-19 to see how safe and effective it is at reducing inflammation and cardiac damage. In summary, these results from animal and human studies strongly indicate that AR can cause cardiovascular complications in diabetes. The current multi-center, large-scale randomized human study of the newly developed powerful ARI may prove its role in diabetic cardiovascular disease to establish therapeutic potential. During the recent coronavirus disease (COVID-19) outbreak in 2019, diabetes and cardiovascular disease were risk factors for severely negative clinical outcomes in patients with COVID19. New data shows that diabetes and obesity are among the strongest predictors of COVID-19 hospitalization. Patients and risk factors for severe morbidity and mortality of COVID- 19.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.

Evolution of modern age drug discovery of lipopeptides and computer-aided drug discovery in India

The endeavor has been attempted to present a review on the evolution of modern age drug discovery in India. The contribution of next generation therapeutics options microbial metabolites and the computational drug discovery aspects to the global market from India have been represented. Microbial metabolites such as lipopeptides and peptide therapeutics are gaining worldwide importance due to their multiple applications as broad-spectrum antimicrobial, antiviral, anticancer properties etc. Due to the surge of microbial resistance, tumor resistance, and ongoing pandemic due to constantly mutating corona virus, there is a need to develop next-generation therapeutics options from natural origin, less toxic to the environment, and have higher specificity towards target. Small molecule therapeutics are certainly less specific towards cancer targets hence the cytotoxicity is a major issue in cancer treatment while drug resistance due to the mutations are coming as challenges every day for drug discovery researchers. Microbial lipopeptide reserves a sweet spot in between the small molecule inhibitors and peptide therapeutics because of their amphiphilic compounds consist of a fatty acid side chain and a cyclic peptide moiety of hydrophilic nature. The computational drug discovery approach accelerates the drug discovery process due to the advancement in supercomputer facilities provided by various funding agencies such as the Department of Biotechnology (DBT) and the Department of Science and Technology (DST) in India. The current review article is focusing light on the research contribution of Indian Scientists and Govt. of India in the field of lipopeptide-based research and applications of Computer-aided drug discovery. © 2022, National Institute of Science Communication and Information Resources. All rights reserved.

Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates.

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) was first identified in the province of Wuhan, China. Since then, there have been over 400 million confirmed cases and 5.8 million deaths by COVID-19 reported worldwide. The urgent need for therapies against SARS-CoV-2 led researchers to use drug repurposing approaches. This strategy allows the reduction in risks, time, and costs associated with drug development. In many cases, a repurposed drug can enter directly to preclinical testing and clinical trials, thus accelerating the whole drug discovery process. In this work, we will give a general overview of the main developments in COVID-19 treatment, focusing on the contribution of the drug repurposing paradigm to find effective drugs against this disease. Finally, we will present our findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19. To our knowledge, seven of these drugs have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in COVID-19 treatment.

Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a substantial increase in mortality and economic and social disruption. The absence of US Food and Drug Administration-approved drugs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for new therapeutic drugs to combat COVID-19. METHODS: The present study proposed a fuzzy hierarchical optimization framework for identifying potential antiviral targets for COVID-19. The objectives in the decision-making problem were not only to evaluate the elimination of the virus growth, but also to minimize side effects causing treatment. The identified candidate targets could promote processes of drug discovery and development. SIGNIFICANT FINDINGS: Our gene-centric method revealed that dihydroorotate dehydrogenase (DHODH) inhibition could reduce viral biomass growth and metabolic deviation by 99.4% and 65.6%, respectively, and increase cell viability by 70.4%. We also identified two-target combinations that could completely block viral biomass growth and more effectively prevent metabolic deviation. We also discovered that the inhibition of two antiviral metabolites, cytidine triphosphate (CTP) and uridine-5'-triphosphate (UTP), exhibits effects similar to those of molnupiravir, which is undergoing phase III clinical trials. Our predictions also indicate that CTP and UTP inhibition blocks viral RNA replication through a similar mechanism to that of molnupiravir.

An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor.

Designing covalent inhibitors is increasingly important, although it remains challenging. Here, we present covalentizer, a computational pipeline for identifying irreversible inhibitors based on structures of targets with non-covalent binders. Through covalent docking of tailored focused libraries, we identify candidates that can bind covalently to a nearby cysteine while preserving the interactions of the original molecule. We found âˆ¼11,000 cysteines proximal to a ligand across 8,386 complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In a prospective evaluation, five out of nine predicted covalent kinase inhibitors showed half-maximal inhibitory concentration (IC50) values between 155 nM and 4.5 µM. Application against an existing SARS-CoV Mpro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC50 values against SARS-CoV-2 Mpro. The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.

A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19.

In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300-400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.