An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Identification of small molecular inhibitors of SIRT3 by computational and biochemical approaches a potential target of breast cancer

Sirtuin 3 (SIRT3) belongs to the Sirtuin protein family, which consists of NAD+-dependent lysine deacylase, involved in the regulation of various cellular activities. Dysregulation of SIRT3 activity has been linked to several types of cancer, including breast cancer. Because of its ability to stimulate adaptive metabolic pathways, it can aid in the survival and proliferation of breast cancer cells. Finding new chemical compounds targeted towards SIRT3 was the primary goal of the current investigation. Virtual screening of ~ 800 compounds using molecular docking techniques yielded 8 active hits with favorable binding affinities and poses. Docking studies verified that the final eight compounds formed stable contacts with the catalytic domain of SIRT3. Those compounds have good pharmacokinetic/dynamic properties and gastrointestinal absorption. Based on excellent pharmacokinetic and pharmacodynamic properties, two compounds (MI-44 and MI-217) were subjected to MD simulation. Upon drug interaction, molecular dynamics simulations demonstrate mild alterations in the structure of proteins and stability. Binding free energy calculations revealed that compounds MI-44 (- 45.61 ± 0.064 kcal/mol) and MI-217 (- 41.65 ± 0.089 kcal/mol) showed the maximum energy, suggesting an intense preference for the SIRT3 catalytic site for attachment. The in-vitro MTT assay on breast cancer cell line (MDA-MB-231) and an apoptotic assay for these potential compounds (MI-44/MI-217) was also performed, with flow cytometry to determine the compound's ability to cause apoptosis in breast cancer cells. The percentage of apoptotic cells (including early and late apoptotic cells) increased from 1.94% in control to 79.37% for MI-44 and 85.37% for MI-217 at 15 μM. Apoptotic cell death was effectively induced by these two compounds in a flow cytometry assay indicating them as a good inhibitor of human SIRT3. Based on our findings, MI-44 and MI-217 merit additional investigation as possible breast cancer therapeutics.

In Silico Design of Potential Small-Molecule Antibiotic Adjuvants against Salmonella typhimurium Ortho Acetyl Sulphydrylase Synthase to Address Antimicrobial Resistance

The inhibition of O-acetyl sulphydrylase synthase isoforms has been reported to represent a promising approach for the development of antibiotic adjuvants. This occurs via the organism developing an unpaired oxidative stress response, causing a reduction in antibiotic resistance in vegetative and swarm cell populations. This consequently increases the effectiveness of conventional antibiotics at lower doses. This study aimed to predict potential inhibitors of Salmonella typhimurium ortho acetyl sulphydrylase synthase (StOASS), which has lower binding energy than the cocrystalized ligand pyridoxal 5 phosphate (PLP), using a computer-aided drug design approach including pharmacophore modeling, virtual screening, and in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation. The screening and molecular docking of 4254 compounds obtained from the PubChem database were carried out using AutoDock vina, while a post-screening analysis was carried out using Discovery Studio. The best three hits were compounds with the PubChem IDs 118614633, 135715279, and 155773276, possessing binding affinities of -9.1, -8.9, and -8.8 kcal/mol, respectively. The in silico ADMET prediction showed that the pharmacokinetic properties of the best hits were relatively good. The optimization of the best three hits via scaffold hopping gave rise to 187 compounds, and they were docked against StOASS; this revealed that lead compound 1 had the lowest binding energy (-9.3 kcal/mol) and performed better than its parent compound 155773276. Lead compound 1, with the best binding affinity, has a hydroxyl group in its structure and a change in the core heterocycle of its parent compound to benzimidazole, and pyrimidine introduces a synergistic effect and consequently increases the binding energy. The stability of the best hit and optimized compound at the StOASS active site was determined using RMSD, RMSF, radius of gyration, and SASA plots generated from a molecular dynamics simulation. The MD simulation results were also used to monitor how the introduction of new functional groups of optimized compounds contributes to the stability of ligands at the target active site. The improved binding affinity of these compounds compared to PLP and their toxicity profile, which is predicted to be mild, highlights them as good inhibitors of StOASS, and hence, possible antimicrobial adjuvants.

Potentials and future perspectives of multi-target drugs in cancer treatment: the next generation anti-cancer agents

Cancer is a major public health problem worldwide with more than an estimated 19.3 million new cases in 2020. The occurrence rises dramatically with age, and the overall risk accumulation is combined with the tendency for cellular repair mechanisms to be less effective in older individuals. Conventional cancer treatments, such as radiotherapy, surgery, and chemotherapy, have been used for decades to combat cancer. However, the emergence of novel fields of cancer research has led to the exploration of innovative treatment approaches focused on immunotherapy, epigenetic therapy, targeted therapy, multi-omics, and also multi-target therapy. The hypothesis was based on that drugs designed to act against individual targets cannot usually battle multigenic diseases like cancer. Multi-target therapies, either in combination or sequential order, have been recommended to combat acquired and intrinsic resistance to anti-cancer treatments. Several studies focused on multi-targeting treatments due to their advantages include; overcoming clonal heterogeneity, lower risk of multi-drug resistance (MDR), decreased drug toxicity, and thereby lower side effects. In this study, we'll discuss about multi-target drugs, their benefits in improving cancer treatments, and recent advances in the field of multi-targeted drugs. Also, we will study the research that performed clinical trials using multi-target therapeutic agents for cancer treatment.

Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery

Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.

Flavonostilbenes natural hybrids from Rhamnoneuron balansae as potential antitumors targeting ALDH1A1: molecular docking, ADMET, MM-GBSA calculations and molecular dynamics studies

Several studies have linked Cancer stem cells (CSCs) to cancer resistance development to chemotherapy and radiotherapy. ALDH1A1 is a key enzyme that regulates the gene expression of CSCs and creates an immunosuppressive tumor microenvironment. It was reported that quercetin and resveratrol were among the inhibitors of ALDH1A1. In early 2022, it was reported that new 11 flavonostilbenes (rhamnoneuronal D-N) were isolated from Rhamnoneuron balansae as potential antiaging natural products. Rhamnoneuronal H (5) could be envisioned as a natural hybrid of quercetin and resveratrol. It was therefore hypothesized that 5 and its analogous isolates rhamnoneuronal D-G (1-4) and rhamnoneuronal I-N (6-11) would have potential ALDH1A1 inhibitory activity. To this end, all isolates were subjected to molecular docking, MM-GBSA, ADMET, and molecular dynamics simulations studies to assess their potential as new leads for cancer treatment targeting ALDH1A1. In silico findings revealed that natural hybrid 5 has a similar binding affinity, judged by MM-GBSA, to the ALDH1A1 active site when compared to the co-crystalized ligand (-64.71 kcal/mole and -64.12 kcal/mole, respectively). Despite having lesser affinity than that of the co-crystalized ligand, the rest of the flavonostilbenes, except 2-4, displayed better binding affinities (-37.55 kcal/mole to -58.6 kcal/mole) in comparison to either resveratrol (-34.44 kcal/mole) or quercetin (-36.48 kcal/mole). Molecular dynamic simulations showed that the natural hybrids 1, 5-11 are of satisfactory stability up to 100 ns. ADMET outcomes indicate that these hybrids displayed acceptable properties and hence could represent an ideal starting point for the development of potent ALDH1A1 inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

In-silico characterization of LSDV132 protein divulged its BCL-2-like nature

Lumpy skin disease virus (LSDV) belongs to Poxviridae family. This virus possesses various proteins which impart potential functions to it including assembly of newly synthesized viruses in the replication cycle and forming their structure. LSDV132 protein is also one of such proteins. Its key characteristics were unknown because, no any relevant study was reported about it. This study aimed to investigate its characteristic features and essential functions using several bioinformatics techniques. These analyses included physiochemical characterization and exploring the crucial functional and structural perspectives. Upon analysis of the physiochemical properties, the instability index was computed to be 30.89% which proposed LSDV132 protein to be a stable protein. Afterwards, the phosphorylation sites were explored. Several sites were found in this regard which led to the hypothesis that it might be involved in the regulation of apoptosis and cell signaling, among other cellular processes. Furthermore, the KEGG analysis and the analysis of protein family classification confirmed that the LSDV132 protein possessed Poxvirus-BCL-2-like motifs, indicating that it might be responsible in modulating the apoptosis of host cells. This crucial finding suggested that the protein under study possessed BCL-2-like features. Proceeding this very important finding, the molecular docking analysis was performed. In this context, various viral BCL-2 inhibitors were retrieved from the ChEMBL database for docking purpose. The docking results revealed that pelcitoclax exhibited best docking scores i.e., -9.1841 kcal/mol, among all of the other docked complexes. This fact signified that this compound might serve as an inhibitor of LSDV132 protein.

Taming the cytokine storm: small molecule inhibitors targeting IL-6/IL-6α receptor

Given the increasing effectiveness of immune-based therapies, management of their associated toxicities is of utmost importance. Cytokine release syndrome (CRS), characterized by elevated levels of cytokine, poses a significant challenge following the administration of antibodies and CAR-T cell therapies. CRS also contributes to multiple organ dysfunction in severe viral infections, notably in COVID-19. Given the pivotal role of IL-6 cytokine in initiating CRS, it has been considered a most potential therapeutic target to mitigate hyperactivated immune responses. While monoclonal antibodies of IL-6 show promise in mitigating cytokine storm, concerns about immunotoxicity persist, and small molecule IL-6 antagonists remain unavailable. The present study employed sophisticated computational techniques to identify potential hit compounds as IL-6 inhibitors, with the aim of inhibiting IL-6/IL-6R protein-protein interactions. Through ligand-based pharmacophore mapping and shape similarity in combination with docking-based screening, we identified nine hit compounds with diverse chemical scaffolds as potential binders of IL-6. Further, the MD simulation of 300 ns of five virtual hits in a complex with IL-6 was employed to study the dynamic behavior. To provide a more precise prediction, binding free energy was also estimated. The identified compounds persistently interacted with the residues lining the binding site of the IL-6 protein. These compounds displayed low binding energy during MMPBSA calculations, substantiating their strong association with IL-6. This study suggests promising scaffolds as potential inhibitors of IL-6/IL-6R protein-protein interactions and provides direction for lead optimization.

Identification of potential inhibitor against Leishmania donovani mitochondrial DNA primase through in-silico and in vitro drug repurposing approaches

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor

O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.

New Indole-6-Carboxylic Acid Derivatives as Multi-Target Antiproliferative Agents: Synthesis, in Silico Studies, and Cytotoxicity Evaluation

Epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor (VEGFR) are commonly overexpressed in cancers making them appealing targets for cancer therapeutics. Two groups of indole-6-carboxylic acid derivatives, hydrazone derivatives targeting EGFR and oxadiazole derivatives targeting VEGFR-2, were synthesized and characterized using FT-IR, 1 H-NMR, 13 CNMR, and HR-MS techniques. Binding patterns to potential molecular targets were studied using molecular docking and compared to standard EGFR and VEGFR-2 inhibitors. The newly synthesized compounds were cytotoxic to the three cancer cell lines tested (HCT-116, HeLa, and HT-29 cell lines) as evaluated by the MTT assay. Compound 3 b (EGFR-targeting) and compound 6 e (VEGFR-2-targeting) possessed the highest antiproliferation activity, were cancer-selective, arrested cancer cells in the G2/M phase, induced the extrinsic apoptosis pathway, and had the highest EGFR/VEGFR-2 enzyme inhibitory activity, respectively. The structure-activity relationships of the new compounds showed that the presence of an aryl or heteroaryl fragment attached to a linker is required for the anti-tumor activity. In conclusion, the findings of the current study suggest that compounds 3 b and 6 e are promising cytotoxic agents that act by inhibiting EGFR and VEGFR-2 tyrosine kinases, respectively.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.

Natural sapogenins as potential inhibitors of aquaporins for targeted cancer therapy: computational insights into binding and inhibition mechanism

Aquaporins (AQPs) are membrane proteins that facilitate the transport of water and other small molecules across biological membranes. AQPs are involved in various physiological processes and pathological conditions, including cancer, making them as potential targets for anticancer therapy. However, the development of selective and effective inhibitors of AQPs remains a challenge. In this study, we explored the possibility of using natural sapogenins, a class of plant-derived aglycones of saponins with diverse biological activities, as potential inhibitors of AQPs. We performed molecular docking, dynamics simulation and binding energy calculation to investigate the binding and inhibition mechanism of 19 sapogenins against 13 AQPs (AQP0-AQP13) that are overexpressed in various cancers. Our results showed that out of 19 sapogenins, 8 (Diosgenin, Gitogenin, Tigogenin, Ruscogenin, Yamogenin, Hecogenin, Sarsasapogenin and Smilagenin) exhibited acceptable drug-like characteristics. These sapogenin also exhibited favourable binding affinities in the range of -7.6 to -13.4 kcal/mol, and interactions within the AQP binding sites. Furthermore, MD simulations provided insights into stability and dynamics of the sapogenin-AQP complexes. Most of the fluctuations in binding pocket were observed for AQP0-Gitogenin and AQP4-Diosgenin. However, remaining protein-ligand complex showed stable root mean square deviation (RMSD) plots, strong hydrogen bonding interactions, stable solvent-accessible surface area (SASA) values and minimum distance to the receptor. These observations suggest that natural sapogenin hold promise as novel inhibitors of AQPs, offering a basis for the development of innovative therapeutic agents for cancer treatment. However, further validation of the identified compounds through experiments is essential for translating these findings into therapeutic applications.Communicated by Ramaswamy H. Sarma.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Overcoming class imbalance in drug discovery problems: Graph neural networks and balancing approaches

This research investigates the application of Graph Neural Networks (GNNs) to enhance the cost-effectiveness of drug development, addressing the limitations of cost and time. Class imbalances within classification datasets, such as the discrepancy between active and inactive compounds, give rise to difficulties that can be resolved through strategies like oversampling, undersampling, and manipulation of the loss function. A comparison is conducted between three distinct datasets using three different GNN architectures. This benchmarking research can steer future investigations and enhance the efficacy of GNNs in drug discovery and design. Three hundred models for each combination of architecture and dataset were trained using hyperparameter tuning techniques and evaluated using a range of metrics. Notably, the oversampling technique outperforms eight experiments, showcasing its potential. While balancing techniques boost imbalanced dataset models, their efficacy depends on dataset specifics and problem type. Although oversampling aids molecular graph datasets, more research is needed to optimize its usage and explore other class imbalance solutions.

Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2

The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.

SwissParam 2023: A Modern Web-Based Tool for Efficient Small Molecule Parametrization

Most steps of drug discovery are now routinely supported and accelerated by computer-aided drug design tools. Among them, structure-based approaches use the three-dimensional structure of the targeted biomacromolecule as a major source of information. When it comes to calculating the interactions of small molecules with proteins using the equations of molecular mechanics, topologies, atom typing, and force field parameters are required. However, generating parameters for small molecules remains challenging due to the large number of existing chemical groups. The SwissParam web tool was first released in 2011 with the aim of generating parameters and topologies for small molecules based on the Merck molecular force field (MMFF) while being compatible with the CHARMM22/27 force field. Here, we present an updated version of SwissParam, providing various new features, including the possibility to setup covalent ligands. Molecules can now be imported from different file formats or via a molecular sketcher. The MMFF-based approach has been updated to provide parameters and topologies compatible with the CHARMM36 force field. An option was added to generate small molecule parametrizations following the CHARMM General Force Field via the multipurpose atom-typer for CHARMM (MATCH) approach. Additionally, SwissParam now generates information on probable alternative tautomers and protonation states of the query molecule so that the user can consider all microspecies relevant to its compound. The new version of SwissParam is freely available at www.swissparam.ch and can also be accessed through a newly implemented command-line interface.

Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

A new calicivirus isolated from a walrus was reported in 2004. Since unknown marine mammalian zoonotic viruses could pose great risks to human health, this study aimed to develop therapeutic countermeasures to quell any potential outbreak of a pandemic caused by this virus. We first generated a 3D model of the walrus calicivirus capsid protein and identified compounds from marine natural products, especially phlorotannins, as potential walrus calicivirus inhibitors. A 3D model of the target protein was generated using homology modeling based on two publicly available template sequences. The sequence of the capsid protein exhibited 31.3% identity and 42.7% similarity with the reference templates. The accuracy and reliability of the predicted residues were validated via Ramachandran plotting. Molecular docking simulations were performed between the capsid protein 3D model and 17 phlorotannins. Among them, five phlorotannins demonstrated markedly stable docking profiles; in particular, 2,7-phloroglucinol-6,6-bieckol showed favorable structural integrity and stability during molecular dynamics simulations. The results indicate that the phlorotannins are promising walrus calicivirus inhibitors. Overall, the study findings showcase the rapid turnaround of in silico-based drug discovery approaches, providing useful insights for developing potential therapies against novel pathogenic viruses, especially when the 3D structures of the viruses remain experimentally unknown.

Napthyridine-derived compounds as promising inhibitors for Staphylococcus aureus CrtM: a primer for the discovery of potential anti-Staphylococcus aureus agents

The disease-free existence of humans is constantly under attack by a variety of infections caused by a variety of organisms including bacteria. Notable among the bacteria is Staphylococcus aureus which is an etiological organism for infections including impetigo, folliculitis, and furuncles. The response of the human immune system against this disease is often neutralized by the production of a pigment called Staphyloxanthin (STX) via a series of reactions mediated by several enzymes. Among these enzymes, dehydrosqualene synthase, also known as CrtM, has emerged as a viable drug target due to its role in mediating the first step of the pathway. Consequently, this study employs molecular modeling approaches including molecular docking, quantum mechanical calculations, and molecular dynamics (MD) simulations among others to investigate the potential of napthyridine derivatives to serve as inhibitors of the CrtM. The results of the study revealed the high binding affinities of the compounds for the target as demonstrated by their docking scores, while further subjection to screening pipeline aimed at determining their fitness for development into drugs revealed just one compound namely 6-[[1-[(2-fluorophenyl) methyl]triazol-4-yl]methoxy]-4-oxo-1H-1,5-naphthyridine-3-carboxylic acid as the compound with good drug-like, pharmacokinetics, and toxicity properties profiles. A 100 ns-long MD simulation of the complexes formed after molecular docking revealed the stable interaction of the compound with the target. Ultimately, this study can be a promising outlet to discover a weapon to fight against clinically resistant bacteria, however, further experimental studies are suggested to carry out in the wet lab, pre-clinical, and clinical levels.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

CBPDdb: a curated database of compounds derived from Coumarin-Benzothiazole-Pyrazole

The present article describes the building of a small-molecule web server, CBPDdb, employing R-shiny. For the generation of the web server, three compounds were chosen, namely coumarin, benzothiazole and pyrazole, and their derivatives were curated from the literature. The two-dimensional (2D) structures were drawn using ChemDraw, and the .sdf file was created employing Discovery Studio Visualizer v2017. These compounds were read on the R-shiny app using ChemmineR, and the dataframe consisting of a total of 1146 compounds was generated and manipulated employing the dplyr package. The web server is provided with JSME 2D sketcher. The descriptors of the compounds are obtained using propOB with a filter. The users can download the filtered data in the .csv and .sdf formats, and the entire dataset of a compound can be downloaded in .sdf format. This web server facilitates the researchers to screen plausible inhibitors for different diseases. Additionally, the method used in building the web server can be adapted for developing other small-molecule databases (web servers) in RStudio. Database URL: https://srampogu.shinyapps.io/CBPDdb_Revised/.

Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches

The SARS-CoV-2 3CLpro protein is one of the key therapeutic targets of interest for COVID-19 due to its critical role in viral replication, various high-quality protein crystal structures, and as a basis for computationally screening for compounds with improved inhibitory activity, bioavailability, and ADMETox properties. The ChEMBL and PubChem database contains experimental data from screening small molecules against SARS-CoV-2 3CLpro, which expands the opportunity to learn the pattern and design a computational model that can predict the potency of any drug compound against coronavirus before in-vitro and in-vivo testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem. The F1-score for inactive and active compounds was 93% and 94%, respectively. SHAP (SHapley Additive exPlanations) on XGB classifier to find important fingerprints from the PaDEL descriptors for this task. The results indicated that the PaDEL descriptors were effective in predicting bioactivity, the proposed neural network design was efficient, and the Explanatory factor through SHAP correctly identified the important fingertips. In addition, we validated the effectiveness of our proposed model using a large dataset encompassing over 100,000 molecules. This research employed various molecular descriptors to discover the optimal one for this task. To evaluate the effectiveness of these possible medications against SARS-CoV-2, more in-vitro and in-vivo research is required.

Insight into novel inhibitors from Sterculia urens against Cholera via pharmacoinformatics and molecular dynamics simulation approaches

The underdeveloped countries with large populations are facing a grave global threat in the form of cholera. Vibrio cholerae, the etiologic agent of Cholera has drawn attention recently due to antimicrobial resistance and resulting outbreaks that necessitates establishment of novel medications to counteract virulence and viability of the pathogen. Sterculia urens Roxb. (Malvaceae) is an ethnomedicinally important tree, which harbors a good number of bioactive phytocompounds. In the present study, 53 phytocompounds of S. urens were screened against the promising target ToxT of V. cholerae employing structure-based drug design approach that revealed three lead compounds, viz., 4,4,5,8-Tetramethylchroman-2-ol (-8.2 kcal/mol), Beta-Bisabolol (-8.2 kcal/mol) and Ledol (-8.7 kcal/mol) with satisfactory ADMET properties. Molecular dynamics simulation for 150 ns unveiled notable compactness and structural stability for the lead compounds considering RMSD, RMSF, Rg, MolSA, PSA and protein-ligand contacts parameters. Molecular mechanics-based MM/GBSA binding energy calculation revealed Beta-Bisabolol (-66.74 kcal/mol) to have better scores than 4,4,5,8-Tetramethylchroman-2-ol (-47.42 kcal/mol) and Ledol (-65.79 kcal/mol). Enzymes were mostly found as drug target class, and Nabilone was found as a structurally similar analog for 4,4,5,8-Tetramethylchroman-2-ol. These discoveries could aid in revealing new antibacterial medications targeting ToxT to combat Cholera.Communicated by Ramaswamy H. Sarma.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

In Silico Discovery of Small-Molecule Inhibitors Targeting SARS-CoV-2 Main Protease

The COVID-19 pandemic has caused severe health threat globally, and novel SARS-Cov-2 inhibitors are urgently needed for antiviral treatment. The main protease (M^pro) of the virus is one of the most effective and conserved targets for anti-SARS-CoV-2 drug development. In this study, we utilized a molecular docking-based virtual screening approach against the conserved catalytic site to identify small-molecule inhibitors of SARS-CoV-2 M^pro. Further biological evaluation helped us identify two compounds, AF-399/40713777 and AI-942/42301830, with moderate inhibitory activity. Besides that, the in silico data, including molecular dynamics (MD) simulation, binding free energy calculations, and AMDET profiles, suggested that these two hits could serve as the starting point for the future development of COVID-19 intervention treatments.

El-Khayatt A. Molecular docking and dynamics simulation of main protease of SARS-CoV-2 with naproxen derivative

Naproxen is a well-known anti-inflammatory drug that is frequently used to relieve inflammation, stiffness, and swelling. Naproxen has previously demonstrated antiviral activity, particularly against the influenza-A virus. There have been previous studies regarding naproxen effect on SARS-CoV-2 infection. Therefore, it is of interest to document the molecular docking and dynamics simulation data of main protease of SARS-CoV-2 with naproxen derivative for further consideration.

Molecular Property Prediction by Combining LSTM and GAT

Molecular property prediction is an important direction in computer-aided drug design. In this paper, to fully explore the information from SMILE stings and graph data of molecules, we combined the SALSTM and GAT methods in order to mine the feature information of molecules from sequences and graphs. The embedding atoms are obtained through SALSTM, firstly using SMILES strings, and they are combined with graph node features and fed into the GAT to extract the global molecular representation. At the same time, data augmentation is added to enlarge the training dataset and improve the performance of the model. Finally, to enhance the interpretability of the model, the attention layers of both models are fused together to highlight the key atoms. Comparison with other graph-based and sequence-based methods, for multiple datasets, shows that our method can achieve high prediction accuracy with good generalizability.

Molecular modelling and biological evaluation of phyto-molecules as potential activators of gluconolactone oxidase (GULO)

Diabetes, the cause of colossal economic and disease burden, is a key area of research in drug discovery programs. Elevated blood glucose levels in diabetes lead to several adverse consequences due to the formation of advanced glycation end products and free radicals. Vitamin C, a potent antioxidant, protects the body's cells and tissues from oxidative damage and dysfunctions. Glucose is the precursor of Vitamin C synthesis in plants and some mammals. L-gulono lactone oxidase (GULO) is the rate-limiting enzyme in producing Vitamin C. However, it is not synthesized in bats, primates, humans, and guinea pigs because of the pseudogene. Several phytomolecules having antioxidant properties are hypothesized to be promising and selective activators of GULO. Therefore, the present study focused on screening agonists of GULO from phytomolecules as an effective augmentor for Vitamin C synthesis, thereby suppressing the sequela of diabetic events. The 3D structure of GULO was generated by the ab-initio method. Subsequently, molecular docking explored the possible binding patterns of GULO protein with different plant phenolic compounds, followed by supplementation of the potent phytomolecules to diabetic guinea pigs. It is noteworthy that Resveratrol and Hydroxytyrosol showed better binding affinity. The molecular simulation also confirmed that Resveratrol is an activator of the GULO enzyme. Interestingly, it was also established that Vitamin C levels were improved in diabetic guinea pigs supplemented with the phytomolecules and comparatively Resveratrol modulates the concentration of glucose and Vitamin C levels substantially, thereby alleviating hyperglycemia. However, further studies are warranted to study the mechanisms.Communicated by Ramaswamy H. Sarma.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Structural Analysis, Multi-Conformation Virtual Screening and Molecular Simulation to Identify Potential Inhibitors Targeting pS273R Proteases of African Swine Fever Virus

The African Swine Fever virus (ASFV) causes an infectious viral disease in pigs of all ages. The development of antiviral drugs primarily aimed at inhibition of proteases required for the proteolysis of viral polyproteins. In this study, the conformation of the pS273R protease in physiological states were investigated, virtually screened the multi-protein conformation of pS273R target proteins, combined various molecular docking scoring functions, and identified five potential drugs from the Food and Drug Administration drug library that may inhibit pS273R. Subsequent validation of the dynamic interactions of pS273R with the five putative inhibitors was achieved using molecular dynamics simulations and binding free energy calculations using the molecular mechanics/Poison-Boltzmann (Generalized Born) (MM/PB(GB)SA) surface area. These findings demonstrate that the arm domain and Thr159-Lys167 loop region of pS273R are significantly more flexible compared to the core structural domain, and the Thr159-Lys167 loop region can serve as a "gatekeeper" in the substrate channel. Leucovorin, Carboprost, Protirelin, Flavin Mononucleotide, and Lovastatin Acid all have Gibbs binding free energies with pS273R that were less than -20 Kcal/mol according to the MM/PBSA analyses. In contrast to pS273R in the free energy landscape, the inhibitor and drug complexes of pS273R showed distinct structural group distributions. These five drugs may be used as potential inhibitors of pS273R and may serve as future drug candidates for treating ASFV.

Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation

The new coronavirus SARS-COV-2, which emerged in late 2019 from Wuhan city of China was regarded as causing agent of the COVID-19 pandemic. The primary protease which is also known by various synonymous i.e., main protease, 3-Chymotrypsin-like protease (3CL^PRO) has a vital role in the replication of the virus, which can be used as a potential drug target. The current study aimed to identify novel phytochemical therapeutics for 3CL^PRO by machine learning-based virtual screening. A total of 4,000 phytochemicals were collected from deep literature surveys and various other sources. The 2D structures of these phytochemicals were retrieved from the PubChem database, and with the use of a molecular operating environment, 2D descriptors were calculated. Machine learning-based virtual screening was performed to predict the active phytochemicals against the SARS-CoV-2 3CL^PRO. Random forest achieved 98% accuracy on the train and test set among the different machine learning algorithms. Random forest model was used to screen 4,000 phytochemicals which leads to the identification of 26 inhibitors against the 3CL^PRO. These hits were then docked into the active site of 3CL^PRO. Based on docking scores and protein-ligand interactions, MD simulations have been performed using 100 ns for the top 5 novel inhibitors, ivermectin, and the APO state of 3CL^PRO. The post-dynamic analysis i.e,. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis reveal that our newly identified phytochemicals form significant interactions in the binding pocket of 3CL^PRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy

The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20-30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the "reliable range" of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Anti-HIV Ermiasolides from Croton megalocarpus

In recent years, elucidation of novel anti-HIV bioactive compounds from natural products is gaining importance rapidly, not only from the research and publications, but also from controlled clinical studies. Here we report three new anti-HIV eudesmane-type sesquiterpenes, 5β-Hydroxy-8α-methoxy eudesm-7(11)-en-12,8-olide (1), 5β,8α-Dihydroxy eudesm-7(11)-en-12,8-olide (2) and 5β-Hydroxy-8H-β-eudesm-7(11)-en-12,8-olide (3). These are trivially named ermiasolide A-C and were isolated from the bark of Croton megalocarpus. 5β-Hydroxy-8α-methoxy eudesm-7(11)-en-12,8-olide (1), showed the highest anti-HIV activity by inhibiting 93% of the viral replication with an IC₅₀ = 0.002 µg/mL. On the other hand, 5β-Hydroxy-8H-β-eudesm-7(11)-en-12,8-olide (3) and 5β,8α-dihydroxy eudesm-7(11)-en-12,8-olide (2), inhibited viral replication by 77.5% at IC₅₀ = 0.04 µg/mL and 69.5% at IC₅₀ = 0.002 µg/mL, respectively. Molecular docking studies showed that the proposed mechanism of action leading to these results is through the inhibition of HIV-protease.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

SARS-CoV-2 Non-Structural Proteins and Their Roles in Host Immune Evasion

Coronavirus disease 2019 (COVID-19) has caused an unprecedented global crisis and continues to threaten public health. The etiological agent of this devastating pandemic outbreak is the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). COVID-19 is characterized by delayed immune responses, followed by exaggerated inflammatory responses. It is well-established that the interferon (IFN) and JAK/STAT signaling pathways constitute the first line of defense against viral and bacterial infections. To achieve viral replication, numerous viruses are able to antagonize or hijack these signaling pathways to attain productive infection, including SARS-CoV-2. Multiple studies document the roles of several non-structural proteins (NSPs) of SARS-CoV-2 that facilitate the establishment of viral replication in host cells via immune escape. In this review, we summarize and highlight the functions and characteristics of SARS-CoV-2 NSPs that confer host immune evasion. The molecular mechanisms mediating immune evasion and the related potential therapeutic strategies for controlling the COVID-19 pandemic are also discussed.

New Chemicals Suppressing SARS-CoV-2 Replication in Cell Culture

Candidates to being inhibitors of the main protease (Mpro) of SARS-CoV-2 were selected from the database of Voronezh State University using molecular modeling. The database contained approximately 19,000 compounds represented by more than 41,000 ligand conformers. These ligands were docked into Mpro using the SOL docking program. For one thousand ligands with best values of the SOL score, the protein–ligand binding enthalpy was calculated by the PM7 quantum-chemical method with the COSMO solvent model. Using the SOL score and the calculated protein–ligand binding enthalpies, eighteen compounds were selected for the experiments. Several of these inhibitors suppressed the replication of the coronavirus in cell culture, and we used the best three among them in the search for chemical analogs. Selection among analogs using the same procedure followed by experiments led to identification of seven inhibitors of the SARS-CoV-2 replication in cell culture with EC50 values at the micromolar level. The identified inhibitors belong to three chemical classes. The three inhibitors, 4,4-dimethyldithioquinoline derivatives, inhibit SARS-CoV-2 replication in Vero E6 cell culture just as effectively as the best published non-covalent inhibitors, and show low cytotoxicity. These results open up a possibility to develop antiviral drugs against the SARS-CoV-2 coronavirus.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Discovery of genistein derivatives as potential SARS-CoV-2 main protease inhibitors by virtual screening, molecular dynamics simulations and ADMET analysis

Background: Due to the constant mutation of virus and the lack of specific therapeutic drugs, the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still poses a huge threat to the health of people, especially those with underlying diseases. Therefore, drug discovery against the SARS-CoV-2 remains of great significance.

Methods: With the main protease of virus as the inhibitor target, 9,614 genistein derivatives were virtually screened by LeDock and AutoDock Vina, and the top 20 compounds with highest normalized scores were obtained. Molecular dynamics simulations were carried out for studying interactions between these 20 compounds and the target protein. The drug-like properties, activity, and ADMET of these compounds were also evaluated by DruLiTo software or online server.

Results: Twenty compounds, including compound 11, were screened by normalized molecular docking, which could bind to the target through multiple non-bonding interactions. Molecular dynamics simulation results showed that compounds 2, 4, 5, 11, 13, 14, 17, and 18 had the best binding force with the target protein of SARS-CoV-2, and the absolute values of binding free energies all exceeded 50 kJ/mol. The drug-likeness properties indicated that a variety of compounds including compound 11 were worthy of further study. The results of bioactivity score prediction found that compounds 11 and 12 had high inhibitory activities against protease, which indicated that these two compounds had the potential to be further developed as COVID-19 inhibitors. Finally, compound 11 showed excellent predictive ADMET properties including high absorption and low toxicity.

Conclusion: These in silico work results show that the preferred compound 11 (ZINC000111282222), which exhibited strong binding to SARS-CoV-2 main protease, acceptable drug-like properties, protease inhibitory activity and ADMET properties, has great promise for further research as a potential therapeutic agent against COVID-19.

Fragment-based design of SARS-CoV-2 Mpro inhibitors

The SARS-CoV-2 virus has been identified as a causative agent for COVID-19 pandemic. About more than 6.3 million fatalities have been attributed to COVID-19 worldwide to date. Finding a viable cure for the illness is urgently needed in light of the present pandemic. The prominence of main protease in the life cycle of virus shapes the main protease as a viable target for design and development of antiviral agents to combat COVID-19. The current study presents the fragment linking strategy to design the novel Mpro inhibitors for COVID-19. A total of 293,451 fragments from diversified libraries have been screened for their binding affinity towards Mpro enzyme. The best 1600 fragment hits were subjected to fragment joining to achieve 100 new molecules using Schrödinger software. The resulting molecules were further screened for their Mpro binding affinity, ADMET, and drug-likeness features. The best 13 molecules were selected, and the first 6 compounds were investigated for their ligand-receptor complex stability through a molecular dynamics study using GROMACS software. The resulting molecules have the potential to be further evaluated for COVID-19 drug discovery.

In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners’ safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics’ data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.

Isoindolin-1-ones Fused to Barbiturates: From Design and Molecular Docking to Synthesis and Urease Inhibitory Evaluation

Helicobacter pylori-induced ulcers and gastric cancer have been one of the main obstacles that the human community has ever struggled with, especially in recent decades. Several different attempts have been made to eradicate this group. One of the most widely used attempts is to inhibit the critical enzyme that facilitates its survival, the urease enzyme. Therefore, in this study, isoindolin-1-ones fused to barbiturates were designed, synthesized, and evaluated for their in vitro urease inhibitory activity as novel inhibitors for the urease enzyme. The synthesis route consisted of two steps. These steps increased the yield rate and decreased the percentage of byproducts while approaching green chemistry using ethanol and water as green solvents and microwave irradiation instead of conventional methods. In vitro urease inhibitory results indicated that all the compounds had higher inhibitory activity than the standard inhibitor, thiourea, and compound 5b proved to be the most potent inhibitor (IC₅₀ = 0.82 ± 0.03 μM). A molecular docking study was performed to understand the interaction between compounds 5a-n and Jack bean urease enzyme. The results of the molecular docking study were also in harmony with the in vitro results, which are discussed in detail later in this study.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.