The Impact of the Scientific Metaverse on the Biotech Industry: How Virtual Reality Helped Researchers Fight Back Against COVID-19

The coronavirus disease 2019 pandemic not only precipitated a digital revolution but also led to one of the largest scientific collaborative open-source initiatives. The EXaSCale smArt pLatform Against paThogEns for CoronaVirus (EXSCALATE4CoV) consortium, led by Dompé farmaceutici S.p.A., brought together 18 global organizations to counter international pandemics more rapidly and efficiently. The consortium also partnered with Nanome, an extended reality software company whose software facilitates the visualization, modification, and simulation of molecules via augmented reality, mixed reality, and virtual reality applications. To characterize the molecular structure of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and to identify promising drug targets, the EXSCALATE4CoV team utilized methods such as homology modeling, molecular dynamics simulations, high-throughput virtual screening, docking, and other computational procedures. Nanome provided analysis of those computational procedures and supplied virtual reality headsets to help scientists better understand and interact with the molecular dynamics and key chemical interactions of SARS-CoV-2. Nanome's collaborative ideation platform enables scientific breakthroughs across research institutions in the fight against the coronavirus pandemic and other diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Investigate the designing of eutectic mixture using DFT computation and evaluate their biological potency using molecular docking

Researchers are exploring the eutectic mixture because of their obvious great potential in various disciplines. Herein, authors have presented the DFT calculations, molecular docking and QSAR results for designed eutectic mixtures (EMs) using thiourea and resorcinol on taking different equivalent ratio. Authors have used Jakob et al. method to determine the melting temperature of the systems or EMs theoretically. Thermodynamic parameteres such as the free energy, enthalpy, and other energy of the EMs at room temperature are determined through DFT calculations using Gaussian. Authors have also calculated the physiochemical descriptors of various eutectic mixture based on DFT calculations. Further, molecular docking of the designed EMs is carried out to investigate their biological potential for inhibition of the Mpro of SARS-CoV-2. © 2023 Elsevier B.V.

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Continuous effort is dedicated to clinically and computationally discovering potential drugs for the novel coronavirus-2. Computer-Aided Drug Design CADD is the backbone of drug discovery, and shifting to computational approaches has become necessary. Quantitative Structure–Activity Relationship QSAR is a widely used approach in predicting the activity of potential molecules and is an early step in drug discovery. 3-chymotrypsin-like-proteinase 3CLpro is a highly conserved enzyme in the coronaviruses characterized by its role in the viral replication cycle. Despite the existence of various vaccines, the development of a new drug for SARS-CoV-2 is a necessity to provide cures to patients. In the pursuit of exploring new potential 3CLpro SARS-CoV-2 inhibitors and contributing to the existing literature, this work opted to build and compare three models of QSAR to correlate between the molecules' structure and their activity: IC50 through the application of Multiple Linear Regression(MLR), Support Vector Regression(SVR), and Particle Swarm Optimization-SVR algorithms (PSO-SVR). The database contains 71 novel derivatives of ML300which have proven nanomolar activity against the 3CLpro enzyme, and the GA algorithm obtained the representative descriptors. The built models were plotted and compared following various internal and external validation criteria, and applicability domains for each model were determined. The results demonstrated that the PSO-SVR model performed best in predictive ability and robustness, followed by SVR and MLR. These results also suggest that the branching degree 6 had a strong negative impact, while the moment of inertia X/Z ratio, the fraction of rotatable bonds, autocorrelation ATSm2, Keirshape2, and weighted path of length 2 positively impacted the activity. These outcomes prove that the PSO-SVR model is robust and concrete and paves the way for its prediction abilities for future screening of more significant inhibitors' datasets. © 2023, The Author(s) under exclusive licence to Associação Brasileira de Engenharia Química.

Impact of Drug Repurposing on SARS-Cov-2 Main Protease

Abstract: The recent emergence of the severe acute respiratory disease caused by a novel coronavirus remains a concern posing many challenges to public health and the global economy. The resolved crystal structure of the main protease of SARS-CoV-2 or SCV2 (Mpro) has led to its identification as an attractive target for designing potent antiviral drugs. Herein, we provide a comparative molecular impact of hydroxychloroquine (HCQ), remdesivir, and β-D-N4-Hydroxycytidine (NHC) binding on SCV2 Mpro using various computational approaches like molecular docking and molecular dynamics (MD) simulation. Data analyses showed that HCQ, remdesivir, and NHC binding to SARS-CoV-2 Mpro decrease the protease loop capacity to fluctuate. These binding influences the drugs' optimum orientation in the conformational space of SCV2 Mpro and produce noticeable steric effects on the interactive residues. An increased hydrogen bond formation was observed in SCV2 Mpro–NHC complex with a decreased receptor residence time during NHC binding. The binding mode of remdesivir to SCV2 Mpro differs from other drugs having van der Waals interaction as the force stabilizing protein–remdesivir complex. Electrostatic interaction dominates in the SCV2 Mpro−HCQ and SCV2 Mpro–NHC. Residue Glu166 was highly involved in the stability of remdesivir and NHC binding at the SCV2 Mpro active site, while Asp187 provides stability for HCQ binding. © 2022, Pleiades Publishing, Ltd.

Chemo- and Regioselective Construction of Functionalized Isocoumarin, Flavone, and Isoquinolinedione via a One-pot Reaction of o-Quinol Acetate and Soft Nucleophiles

A one-pot strategy for the synthesis of substituted isocoumarin, flavone, and isoquinolinedione derivatives through a switchable C-arylation/lactonization or SNAr reaction from a wide range of soft nucleophiles and o-quinol acetates has been developed. This base-mediated protocol proceeds under transition-metal-free conditions and selectively affords various heteroarenes in 13–98% yields from readily prepared or commercially available 1,3-dicarbonyl and α-EWG-substituted carbonyl compounds. The synthetic utility is further demonstrated in the synthesis of potential anti-HIV and anti-coronavirus derivatives and COX-2 inhibitors. In addition, detailed experimental and computational studies are performed to provide an intensive understanding and strong support of the reaction mechanism. © 2023 Wiley-VCH GmbH.

SARS-CoV-2 Main Protease Inhibitors: Structure-Based Enhancement to Anti-Viral Pre-Clinical GC376 Encourages Further Development

SARS-CoV-2 Main protease (Mpro) is pivotal in viral replication and transcription. Mpro mediates proteolysis of translated products of replicase genes ORF1a and ORF1ab. Surveying pre-clinical trial Mpro inhibitors suggests potential enhanced efficacy for some moieties. Concordant with promising in vitro and in silico data, the protease inhibitor GC376 was chosen as a lead. Modification of GC376 analogues yielded a series of promising Mpro inhibitors. Design optimization identified compound G59i as lead candidate, displaying a binding energy of −10.54kcal/mol for the complex. Robust interactivity was noted between G59i and Mpro. With commendable ADMET characteristics and enhanced potency, further G59i analysis may be advantageous;moreover, identified key Mpro residues could contribute to the design of neotenic inhibitors. [ FROM AUTHOR]

Investigation on Phyto‐active Constituent of Clerodendrum paniculatum as Therapeutic Agent against Viral Diseases

The ability of alcohol extracted constituents from the leaves of Clerodendrum paniculatum to inhibit SARS‐CoV‐2 was investigated through various computational methods like docking, molecular dynamic simulations and pharmacokinetic predictions. Of the various active constituents, quercetin was identified as a potent inhibitor that can bind strongly to the active site of main protease (Mpro) and spike protein of the virus with respective binding free energy of −41.07 and −40.76 kcal mol−1. Molecular dynamic simulations also supported the binding interactions by the presence of strong hydrogen bonding interactions with the key residues in the binding pocket of the target protein. The results were ascertained experimentally by evaluating the inhibition potential of the extract against spike and Mpro proteins of SARS‐CoV‐2. [ FROM AUTHOR]

Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models.

Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.

Computational demonstration of cheminformatics and machine learning in coronavirus drug discovery

Computational tools in drug discovery involve the use of algorithms in predicting properties of potential drugs as ligands as well as biological targets in structural forms. This dates back to more than 30 years ago and have been perfected with time and advancement of technology. They are reliable to varying extents depending on the nature of the study, complexity among other factors. Computational tools help medicinal chemists, computational chemists, and structural biologists to design and optimize potential drugs as early as possible and reduce or completely avoid attrition in the drug discovery pipeline. The search for drugs to cure or manage COVID-19 is made relatively easier and more efficient by the use of computational tools to help understand the ADMET properties of possible drugs under development. This chapter demonstrates how computational tools in cheminformatics and machine learning can be used in the fight against COVID-19 from a medicinal chemistry perspective using selected parameters. © 2022 Elsevier Inc. All rights reserved.

Artificial intelligence methods to repurpose and discover new drugs to fight the Coronavirus disease-2019 pandemic

The Coronavirus disease 2019 pandemic struck the world at the end of 2019 and, as of 2021, there are no specific drugs available against the causative agent, the severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2). From the onset of the pandemic, researchers have been trying to find drugs among the current therapeutic arsenal that could target crucial viral function, and many of these efforts resulted in clinical trials to repurpose a drug for this new indication. In this scenario, artificial intelligence (AI) is of fundamental importance, allowing academia and pharmaceutical companies to accelerate the discovery of biochemical insights from the chemical and biological information available in literature databases. This chapter will cover some AI methods that are being explored to repurpose drugs against SARS-CoV-2. It will be outlined how these methods work followed by a discussion of selected examples applying them to identify promising drugs. © 2022 Elsevier Inc. All rights reserved.

Quantitative structure–activity relationship-based computational approaches

World Health Organization (WHO) categorized novel Coronavirus disease (COVID-19), triggered by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) as a world pandemic. This infection has been increasing alarmingly by instigating enormous social and economic disturbance. In order to retort rapidly, the inhibitors previously designed against different targets will be a good starting point for anti-SARS-CoV-2 inhibitors. The chapter deals with various quantitative structure–activity relationship (QSAR) techniques currently used in computational drug design and their applications and advantages in the overall drug design process. The chapter reviews current QSAR studies carried out against SARS-COV-2. The QSAR study design is composed of some major facets: (1) classification QSAR-based data mining of various inhibitors, (2) QSAR-based virtual screening to recognize molecules that could be effective against assumed COVID-19 protein targets. (3) Finally validation of hits through receptor–ligand interaction analysis. This approach is used overall to help in the process of COVID-19 drug discovery. It presents key conceptions, sets the stage for QSAR-based screening of active molecules against SARS-COV-2. Moreover, the QSAR models reported can be further used to monitor huge databases. This chapter gives a first-hand review of all the current QSAR parameters developed for generating a good QSAR model against SARS-COV-2 and subsequently designing a drug against the COVID-19 virus. © 2022 Elsevier Inc. All rights reserved.

Computational Chemistry as an Educational Tool in Health Sciences

Experimentation as a teaching technique allows the understanding and relationship of concepts as well as the acquisition of problem-solving skills. Computational chemistry is a tool for studying chemical phenomena through computational experiments. The use of simulation in chemistry and biochemistry education is evolving the teaching techniques and developing computational skills. Teaching chemistry and biology through simulations and structural analysis is mainly limited to graduate students. However, we are moving toward a future where computational skills, including programming and simulation, will no longer be optional. In the present research, we use a pharmaceutical example for computational modeling and molecular docking to study and design drugs. Physicochemical characterization of the drug Remdesivir was carried out to demonstrate that the acquisition and learning of theoretical concepts are more practical when performing computational experiments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Study of Metal–Carbonyl Complexes by Means of Computational IR Spectra Analysis: A Remote Didactic Approach Based on Chemical Thinking

We describe a remote pedagogical approach based on chemical thinking to study metal-carbonyl complexes by analyzing simulated IR spectra. The proposed approach, implemented due to the COVID-19 pandemic, can be employed in classrooms that have very limited laboratory equipment for evaluating toxic metal-carbonyl compounds, as well as for synthesizing compounds that have not been reported . The method, consisting of a class lecture accompanied by a remote computational activity , aims to provide students with the ability to assemble concepts from different fields, such as organometallic chemistry and analytical chemistry, while taking advantage of computational methods to answer higher level questions. We evaluated whether analyzing the nature of M-CO bonding was appropriate for achieving these educational goals. Octahedral compounds of the M(CO)(6) and M(CO)(4)L-2 type, bearing a variety of metal centers (M = Cr, Mo, W, V, Mn and Fe) and ligands (L = phosphines and phosphites), as well as bimetallic Fe-2(CO)(9), were compared, showing how these modifications affect M-CO bonding. After the didactic session, attended by second-year and upper-division students of Facultad de Quimica at UNAM, an evaluation and survey showed that students improved their understanding of the subject when they obtained and visualized IR spectra, also exhibiting greater confidence and enthusiasm for addressing challenging topics. The combination of computational results, spectroscopic analysis, and organometallic theory represents an efficient and clear procedure for implementing chemical thinking, regardless of the difficulties posed by the COVID-19 pandemic.

The Assembly of a Computing Platform for Studying Protein Inhibitors Against COVID-19 Replication

A new highly efficient GPU-equipped computing platform for studying the molecular inhibition mechanisms of the Sars-Cov-2 virus by natural compounds and aptamers has been installed and configured. Studies will be carried out by means of molecular dynamics methods and programs. For this reason, we have assembled specific hardware components into a 4U rack, together with a NVIDIA RTX 3060 GPU for speeding up molecular dynamics calculations and visualizing their outcomes. In fact, not only computational resources, in terms of computing power and execution times, are needed by molecular dynamics programs adopted by us, but also a system allowing the rendering and visualization of large biomolecules and their trajectories, such as viruses and proteins, represents a key factor for our work. Details about platform implementation and preliminary tests carried out are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Bio-Inspired Modelling of Disease Through Delayed Strategies

In 2020, the reported cases were 0.12 million in the six regions to the official report of the World Health Organization (WHO). For most children infected with leprosy, 0.008629 million cases were detected under fifteen. The total infected ratio of the children population is approximately 4.4 million. Due to the COVID-19 pandemic, the awareness programs implementation has been disturbed. Leprosy disease still has a threat and puts people in danger. Nonlinear delayed modeling is critical in various allied sciences, including computational biology, computational chemistry, computational physics, and computational economics, to name a few. The time delay effect in treating leprosy delayed epidemic model is investigated. The whole population is divided into four groups: those who are susceptible, those who have been exposed, those who have been infected, and those who have been vaccinated. The local and global stability of well-known conclusions like the Routh Hurwitz criterion and the Lyapunov function has been proven. The parameters’ sensitivity is also examined. The analytical analysis is supported by computer results that are presented in a variety of ways. The proposed approach in this paper preserves equilibrium points and their stabilities, the existence and uniqueness of solutions, and the computational ease of implementation.

From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples.

Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products' drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.

Bibliometric Analysis of Computational Chemistry Research and Its Correlation with Covid-19 Pandemic

Computational Chemistry is a branch of chemistry that employs computer simulations to assist in resolving problems regarding chemistry. The goal of this research is to combine mapping analysis from VOSviewer software to analyze bibliometrics in Computational Chemistry field. The data were obtained through the use of a reference manager application with "Computational Chemistry" as the keyword. We collected 1000 articles published between 2017 - 2021 in the search results. According to the findings, research in the field of Computational Chemistry decreased from 2018 to 2020, but increased since 2021. The primary reason for this is that the pandemic has had a significant impact on Computational Chemistry, which is related to laboratory engineering and molecular modeling. This study demonstrates the value of bibliometric analysis in providing analytical data about a phenomenon. The findings of this study are beneficial for future research to find potential areas of Computational Chemistry that can be studied further, due to the discovery of less-researched areas. © 2022. All Rights Reserved.

Jiedu Huoxue Decoction for Cytokine Storm and Thrombosis in Severe COVID-19: A Combined Bioinformatics and Computational Chemistry Approach

Jiedu Huoxue Decoction (JHD), a recommended traditional prescription for patients with severe COVID-19, has appeared in the treatment protocols in China. Based on bioinformatics and computational chemistry methods, including molecular docking, molecular dynamics (MD) simulation, and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) calculation, we aimed to reveal the mechanism of JHD in treating severe COVID-19. The compounds in JHD were obtained and screened on TCMSP, SwissADME, and ADMETLab platforms. The compound targets were obtained from TCMSP and STITCH, while COVID-19 targets were obtained from Genecards and NCBI. The protein-protein interaction network was constructed by using STRING. Gene Ontology (GO) and KEGG enrichment were performed with ClueGO and R language. AutoDock vina was employed for molecular docking. 100 ns MD simulation of the optimal docking complex was carried out with AmberTools 20. A total of 84 compounds and 29 potential targets of JHD for COVID-19 were collected. The key phytochemicals included quercetin, luteolin, β-sitosterol, puerarin, stigmasterol, kaempferol, and wogonin, which could regulate the immune system. The hub genes included IL6, IL10, VEGFA, IL1B, CCL2, HMOX1, DPP4, and ACE2. ACE2 and DPP4 were related to SARS-CoV-2 entering cells. GO and KEGG analysis showed that JHD could intervene in cytokine storm and endothelial proliferation and migration related to thrombosis. The molecular docking, 100 ns MD simulation, and MM/GBSA calculation confirmed that targets enriched in the COVID-19 pathway had high affinities with related compounds, and the conformations of the puerarin-ACE2, quercetin-EGFR, luteolin-EGFR, and quercetin-IL1B complexes were stable. In a word, JHD could treat COVID-19 by intervening in cytokine storm, thrombosis, and the entry of SARS-CoV-2, while regulating the immune system. These mechanisms were consistent with JHD's therapeutic concept of “detoxification” and “promoting blood circulation and removing blood stasis” in treating COVID-19. The research provides a theoretical basis for the development and application of JHD. © The Author(s) 2022.

Simulating Electronic Absorption Spectra of Atmospherically Relevant Molecules: A Systematic Assignment for Enhancing Undergraduate STEM Education

Computational and atmospheric chemistry are two important branches of contemporary chemistry. With the present topical nature of climate change and global warming, it is more crucial than ever that students are aware of and exposed to atmospheric chemistry, with an emphasis on how modeling may aid in understanding how atmospherically relevant chemical compounds interact with incoming solar radiation. Nonetheless, computational and atmospheric chemistry are under-represented in most undergraduate chemistry curricula. In this manuscript, we describe a simple and efficient method for simulating the electronic absorption spectral profiles of atmospherically relevant molecules that may be utilized in an undergraduate computer laboratory. The laboratory results give students hands-on experience in computational and atmospheric chemistry, as well as electronic absorption spectroscopy.

Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease.

We present several workflows for protein-ligand docking and free energy calculation for use in the workflow management system Galaxy. The workflows are composed of several widely used open-source tools, including rDock and GROMACS, and can be executed on public infrastructure using either Galaxy's graphical interface or the command line. We demonstrate the utility of the workflows by running a high-throughput virtual screening of around 50000 compounds against the SARS-CoV-2 main protease, a system which has been the subject of intense study in the last year.