In Silico Oncology Drug Repositioning and Polypharmacology

Repurposing of neprilysin inhibitor 'sacubitrilat' as an anti-cancer drug by modulating epigenetic and apoptotic regulators

Modifications in the epigenetic landscape have been considered a hallmark of cancer. Histone deacetylation is one of the crucial epigenetic modulations associated with the aggressive progression of various cancer subtypes. Herein, we have repurposed the neprilysin inhibitor sacubitrilat as a potent anticancer agent using in-silico protein-ligand interaction profiler (PLIP) analysis, molecular docking, and in vitro studies. The screening of PLIP profiles between vorinostat/panobinostat and HDACs/LTA4H followed by molecular docking resulted in five (Sacubitrilat, B65, BDS, BIR, and NPV) FDA-approved, experimental and investigational drugs. Sacubitrilat has demonstrated promising anticancer activity against colorectal cancer (SW-480) and triple-negative breast cancer (MDA-MB-231) cells, with IC₅₀ values of 14.07 μg/mL and 23.02 μg/mL, respectively. FACS analysis revealed that sacubitrilat arrests the cell cycle at the G0/G1 phase and induces apoptotic-mediated cell death in SW-480 cells. In addition, sacubitrilat inhibited HDAC isoforms at the transcriptomic level by 0.7-0.9 fold and at the proteomic level by 0.5-0.6 fold as compared to the control. Sacubitrilat increased the protein expression of tumor-suppressor (p53) and pro-apoptotic makers (Bax and Bid) by 0.2-2.5 fold while decreasing the expression of anti-apoptotic Bcl2 and Nrf2 proteins by 0.2-0.5 fold with respect to control. The observed cleaved PARP product indicates that sacubitrilat induces apoptotic-mediated cell death. This study may pave the way to identify the anticancer potential of sacubitrilat and can be explored in human clinical trials.

A new framework for host-pathogen interaction research

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Artificial Neural Network-Based Study Predicts GS-441524 as a Potential Inhibitor of SARS-CoV-2 Activator Protein Furin: a Polypharmacology Approach

Furin, a pro-protein convertase, plays a significant role as a biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS-CoV-2, furin has become the center of attraction for researchers as the spike protein contains a polybasic furin cleavage site. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antiviral. To enhance the selection of new inhibitors, we employed Kohonen's artificial neural network-based self-organizing maps for ligand-based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies can address the errors generated due to promiscuity/polypharmacology. We found 15 existing FDA antiviral drugs having the potential to inhibit furin. Among these, six compounds have targets on important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA, and PNP). The role of these 15 drugs inhibiting furin can be established by studying further on patients infected with number of viruses including SARS-CoV-2. Here we propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) for repurposing as inhibitors of furin. The best results were observed with GS-441524.

A Network-Biology led Computational Drug repurposing Strategy to prioritize therapeutic options for COVID-19

The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs.

Synthesis, spectroscopic, and computational studies on molecular charge-transfer complex of 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1, 3-diol with chloranilic acid: Potential antiviral activity simulation of CT-complex against SARS-CoV-2

An innovative charge-transfer complex between the Schiff base 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1,3-diol [SAL-THAM] and the π-acceptor, chloranilic acid (CLA) within the mole ratio (1:1) was synthesized and characterized aiming to investigate its electronic transition spectra in acetonitrile (ACN), methanol (MeOH) and ethanol (EtOH) solutions. Applying Job`s method in the three solvents supported the 1:1 (CLA: SAL-THAM) mole ratio complex formation. The formation of stable CT- complex was shown by the highest values of charge-transfer complex formation constants, K_CT, calculated using minimum-maximum absorbance method, with the sequence, acetonitrile > ethanol > methanol DFT study on the synthesized CT complex was applied based on the B3LYP method to evaluate the optimized structure and extract geometrical and reactivity parameters. Based on TD-DFT theory, the electronic properties, ¹H and ¹³C NMR, IR, and UV-Vis spectra of the studied system in different solvents showing good agreement with the experimental studies. MEP map described the possibility of hydrogen bonding and charge transfer in the studied system. Finally, a computational approach for screening the antiviral activity of CT - complex towards SARS-CoV-2 coronavirus protease via molecular docking simulation was conducted and confirmed with molecular dynamic (MD) simulation.

Potential of Marine Terpenoids against SARS-CoV-2: An In Silico Drug Development Approach

In an emergency, drug repurposing is the best alternative option against newly emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, several bioactive natural products have shown potential against SARS-CoV-2 in recent studies. The present study selected sixty-eight broad-spectrum antiviral marine terpenoids and performed molecular docking against two novel SARS-CoV-2 enzymes (main protease or M^pro or 3CL^pro) and RNA-dependent RNA polymerase (RdRp). In addition, the present study analysed the physiochemical-toxicity-pharmacokinetic profile, structural activity relationship, and phylogenetic tree with various computational tools to select the 'lead' candidate. The genomic diversity study with multiple sequence analyses and phylogenetic tree confirmed that the newly emerged SARS-CoV-2 strain was up to 96% structurally similar to existing CoV-strains. Furthermore, the anti-SARS-CoV-2 potency based on a protein-ligand docking score (kcal/mol) exposed that the marine terpenoid brevione F (-8.4) and stachyflin (-8.4) exhibited similar activity with the reference antiviral drugs lopinavir (-8.4) and darunavir (-7.5) against the target SARS-CoV-M^pro. Similarly, marine terpenoids such as xiamycin (-9.3), thyrsiferol (-9.2), liouvilloside B (-8.9), liouvilloside A (-8.8), and stachyflin (-8.7) exhibited comparatively higher docking scores than the referral drug remdesivir (-7.4), and favipiravir (-5.7) against the target SARS-CoV-2-RdRp. The above in silico investigations concluded that stachyflin is the most 'lead' candidate with the most potential against SARS-CoV-2. Previously, stachyflin also exhibited potential activity against HSV-1 and CoV-A59 within IC₅₀, 0.16-0.82 µM. Therefore, some additional pharmacological studies are needed to develop 'stachyflin' as a drug against SARS-CoV-2.

Topological network based drug repurposing for coronavirus 2019

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus's performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.

Repurposing FDA-approved drugs against multiple proteins of SARS-CoV-2: An in silico study

The current crisis of the COVID-19 pandemic around the world has been devastating as many lives have been lost to the novel SARS CoV-2 virus. Thus, there is an urgent need for the right therapeutic drug to curb the disease. However, there is time constraint in drug development, hence the need for drug repurposing approach, a relatively fast and less expensive alternative. In this study, 1,100 Food and Drug Administration (FDA) approved drugs were obtained from DrugBank and trimmed to 791 ligands based on illicitness, withdrawal from the market, being chemical agents rather than drugs, being investigational drugs and having molecular weight greater than 500 (Kg/mol). The ligands were docked against six drug targets of the novel SARS CoV-2 - 3-chymotrypsin-like protease (3CLpro), Angiotensin-converting enzyme (ACE2), ADP ribose phosphatase of NSP3 (NSP3), NSP9 RNA binding protein (NSP9), RNA dependent RNA polymerase (RdRp) and Replicase Polyprotein 1a (RP1a). UCSF Chimera, PyRx and Discovery Studio, were used to prepare the proteins, dock the ligands and visualize the complexes, respectively. Remdesivir, Lopinavir and Hydroxychloroquine were used as reference drugs. Pharmacokinetic properties of the ligands were obtained using AdmetSAR. The binding energies of the standard drugs ranged from -5.4 to -8.7 kcal/mol while over 400 of the ligands screened showed binding energy lower than -5.4 kcal/mol. Out of the 791 number of compounds docked, 10, 91, 132, 92, 54 and 96 compounds showed lower binding energies than all the controls against 3CLPro, ACE2, NSP3, NSP9, RP1a and RdRp, respectively. Ligands that bound all target proteins, and showed the lowest binding energies with good ADMET properties and particularly showed the lowest binding against ACE2 are ethynodiol diacetate (-15.6 kcal/mol), methylnaltrexone (-15.5 kcal/mol), ketazolam (-14.5 kcal/mol) and naloxone (-13.6 kcal/mol). Further investigations are recommended for ethynodiol diacetate, methylnaltrexone, ketazolam and naloxone through preclinical and clinical studies to ascertain their effectiveness.

Drug Repurposing for the Treatment of COVID-19: A Knowledge Graph Approach

Identifying effective drug treatments for COVID-19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID-19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID-19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID-19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID-19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif-discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies.

Drug Repurposing Using Biological Networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis

Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fields. This paper employs a co-word analysis approach to map the intellectual structure of the coronavirus literature for a better understanding of how coronavirus research and the disease itself have developed during the target timeframe. A strategic diagram has been drawn to depict the coronavirus domain's structure and development. A detailed picture of coronavirus literature has been extracted from a huge number of papers to provide a quick overview of the coronavirus literature. The main themes of past coronavirus-related publications are (a) "Antibody-Virus Interactions," (b) "Emerging Infectious Diseases," (c) "Protein Structure-based Drug Design and Antiviral Drug Discovery," (d) "Coronavirus Detection Methods," (e) "Viral Pathogenesis and Immunity," and (f) "Animal Coronaviruses." The emerging infectious diseases are mostly related to fatal diseases (such as Middle East respiratory syndrome, severe acute respiratory syndrome, and COVID-19) and animal coronaviruses (including porcine, turkey, feline, canine, equine, and bovine coronaviruses and infectious bronchitis virus), which are capable of placing animal-dependent industries such as the swine and poultry industries under strong economic pressure. Although considerable research into coronavirus has been done, this unique field has not yet matured sufficiently. Therefore, "Antibody-virus Interactions," "Emerging Infectious Diseases," and "Coronavirus Detection Methods" hold interesting, promising research gaps to be both explored and filled in the future.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

A new precision medicine initiative at the dawn of exascale computing

Which signaling pathway and protein to select to mitigate the patient's expected drug resistance? The number of possibilities facing the physician is massive, and the drug combination should fit the patient status. Here, we briefly review current approaches and data and map an innovative patient-specific strategy to forecast drug resistance targets that centers on parallel (or redundant) proliferation pathways in specialized cells. It considers the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. The construction of the resulting Proliferation Pathway Network Atlas will harness the emerging exascale computing and advanced artificial intelligence (AI) methods for therapeutic development. Merging the resulting set of targets, pathways, and proteins, with current strategies will augment the choice for the attending physicians to thwart resistance.

Repurposing Approved Drugs for Guiding COVID-19 Prophylaxis: A Systematic Review

The SARS-CoV-2 outbreak originally appeared in China in December 2019 and became a global pandemic in March 2020. This infectious disease has directly affected public health and the world economy. Several palliative therapeutic treatments and prophylaxis strategies have been used to control the progress of this viral infection, including pre-(PrEP) and post-exposure prophylaxis. On the other hand, research groups around the world are still studying novel drug prophylaxis and treatment using repurposing approaches, as well as vaccination options, which are in different pre-clinical and clinical testing phases. This systematic review evaluated 1,228 articles from the PubMed and Scopus indexing databases, following the Kitchenham bibliographic searching protocol, with the aim to list drug candidates, potentially approved to be used as new options for SARS-CoV-2 prophylaxis clinical trials and medical protocols. In searching protocol, we used the following keywords: "Covid-19 or SARS-CoV-2" or "Coronavirus or 2019 nCoV," "prophylaxis," "prophylactic," "pre-exposure," "COVID-19 or SARS-CoV-2 Chemoprophylaxis," "repurposed," "strategies," "clinical," "trials," "anti-SARS-CoV-2," "anti-covid-19," "Antiviral," "Therapy prevention in vitro," in cells "and" human testing. After all protocol steps, we selected 60 articles that included: 15 studies with clinical data, 22 studies that used in vitro experiments, seven studies using animal models, and 18 studies performed with in silico experiments. Additionally, we included more 22 compounds between FDA approved drugs and drug-like like molecules, which were tested in large-scale screenings, as well as those repurposed approved drugs with new mechanism of actions. The drugs selected in this review can assist clinical studies and medical guidelines on the rational repurposing of known antiviral drugs for COVID-19 prophylaxis.

Synthesis, anti-bacterial evaluation, DFT study and molecular docking as a potential 3-chymotrypsin-like protease (3CLpro) of SARS-CoV-2 inhibitors of a novel Schiff bases

New Schiff bases {N'-(phenyl(pyridin-2-yl)methylene) isonicotinohydrazide (L¹H), N¹ -(naphthalen-1-yl)-N² -(phenyl(pyridin-2-yl) methylidene) ethane-1,2-diamine (L²H), N-(6-chlorobenzo[d]thiazol-2-yl)-1-phenyl-1-(pyridin-2-yl) methanimine (L³H)}were synthesized by reaction of 2-benzoylpyridine with different amines (2-amino-6-chlorobenzothiazole, isonicotinohydrazide and N ¹-(naphthalen-1-yl)ethane-1,2-diamine) and characterized by ¹H-NMR, ¹³C-NMR, IR mass spectroscopy and elemental analysis. The compounds were assayed by the disc diffusion method for anti-bacterial against five pathogenic bacteria species (Staphylococcus aureus, Micrococcus luteus, Staphylococcus pyogenes, Bacillus subtilis, and E. coli). All prepared Schiff bases showed good activity compared to positive control (streptomycin), Moreover the L³H showed the highest activity against S. aureus, and M. luteus than the other compounds and streptomycin. In additional molecular docking studies with 3-chymotrypsin-like protease (3CLpro), the essential enzyme for SARS-CoV-2 proliferation. The rest of compounds have shown promising results as 3CLpro inhibitors interacting with the active sites of the enzymes. Finally, DFT 's estimated electrostatic molecular potential results were used to illustrate the molecular docking findings. The DFT calculations showed that L³H has the highest dipole moment and electrophilicity index. Interestingly, L²H of the largest energy gap ∆E = 2.49 eV, there are several hydrophilic interactions that could facilitate the binding with the receptors. All of these parameters could be shared to significantly affect the protein sites of binding affinity with different extent.

Credible Protein Targets and Curative Strategies for COVID-19: a Review

The pandemic of coronavirus infection 2019 (COVID-19) due to the serious respiratory condition created by the coronavirus 2 (SARS-CoV-2) presents a challenge to recognize effective strategies for management and treatment. In general, COVID-19 is an acute disease that can also be fatal, with an ongoing 10.2% case morbidity rate. Extreme illness may bring about death because of enormous alveolar damage and hemorrhage along with progressive respiratory failure. The rapidly expanding information with respect to SARS-CoV-2 research suggests a substantial number of potential drug targets. The most encouraging treatment to date is suggested to be with the help of remdesivir, hydroxychloroquine, and many such repurposed drugs. Remdesivir has a strong in vitro activity for SARS-CoV-2, yet it is not the drug of choice as affirmed by the US Food and Drug Administration and presently is being tried in progressing randomized preliminaries. The COVID-19 pandemic has been the worst worldwide general health emergency of this age and, possibly, since the pandemic influenza outbreak of 1918. The speed and volume of clinical preliminaries propelled to examine potential treatments for COVID-19 feature both the need and capacity to create abundant evidence even in the center of a pandemic. No treatments have been demonstrated as accurate and dependable to date. This review presents a concise precise of the targets and broad treatment strategies for the benefit of researchers.

Antitussive noscapine and antiviral drug conjugates as arsenal against COVID-19: a comprehensive chemoinformatics analysis

Coronavirus pandemic has caused a vast number of deaths worldwide. Thus creating an urgent need to develop effective counteragents against novel coronavirus disease (COVID-19). Many antiviral drugs have been repurposed for treatment but implicated minimal recovery, which further advanced the need for clearer insights and innovation to derive effective therapeutics. Strategically, Noscapine, an approved antitussive drug with positive effects on lung linings may show favorable outcomes synergistically with antiviral drugs in trials. Hence, we have theoretically examined the combinatorial drug therapy by culminating the existing experimental results with in silico analyses. We employed the antitussive noscapine in conjugation with antiviral drugs (Chloroquine, Umifenovir, Hydroxychloroquine, Favlplravir and Galidesivir). We found that Noscapine-Hydroxychloroquine (Nos-Hcq) conjugate has strong binding affinity for the main protease (Mpro) of SARS-CoV-2, which performs key biological function in virus infection and progression. Nos-Hcq was analyzed through molecular dynamics simulation. The MD simulation for 100 ns affirmed the stable binding of conjugation unprecedentedly through RMSD and radius of gyration plots along with critical reaction coordinate binding free energy profile. Also, dynamical residue cross-correlation map with principal component analysis depicted the stable binding of Nos-Hcq conjugate to Mpro domains with optimal secondary structure statistics of complex dynamics. Also, we reveal the drugs with stable binding to major domains of Mpro can significantly improve the work profile of reaction coordinates, drug accession and inhibitory regulation of Mpro. The designed combinatorial therapy paves way for further prioritized in vitro and in vivo investigations for drug with robust binding against Mpro of SARS-CoV-2.

Identification of therapeutic target in S2 domain of SARS nCov-2 Spike glycoprotein: Key to design and discover drug candidates for inhibition of viral entry into host cell

Humanity is facing a grieve danger of coronavirus disease-19 caused by severe acute respiratory syndrome novel coronavirus-2 (SARS nCov-2). There is an urgent need of therapeutics that can help in overcoming this global pandemic. Identifying novel therapeutic target and screening already approved drug is a faster approach in this situation. Spike glycoprotein (Sgp) of SARS nCoV-2 is potentials target where in researchers have targeted receptor binding domain (RBD) of S1 domain. The S2 domain of Sgp also plays a pivotal role in viral entry, but the mechanism is less understood. We analyzed the structure of Sgp S2 domain in pre-fusion state and Heptad repeat region in its post-fusion state available from protein data bank. Sgp shows three major regions in S2 domain, the fusion peptide (FP), heptad repeat 1 (HR1) and central helical (CH) region. The HR1 region undergoes structural changes by flipping approximately 180∘ and coil up to form a rod like structure during fusion process implying its role in viral entry into the host cell. This structural change in S2 domain helices is crucial step, if this process is hindered by targeting the HR1 and CH region then the progression of virus can be stopped. Possible binding cavity was identified near the HR1 and CH region in S2 domain and docking-based virtual screening of FDA approved drugs was performed. Promising candidates like Troxerutin, Thymopentin and Daclatasvir can be used as therapeutics provided an immediate in-vitro and clinical studies are carried out by research groups.

Drug targets for COVID-19 therapeutics: Ongoing global efforts

The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footing to identify the best drug combination to address the disease. In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described. The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health.

Computational insights into tetracyclines as inhibitors against SARS-CoV-2 Mpro via combinatorial molecular simulation calculations

The COVID-19 pandemic raised by SARS-CoV-2 is a public health emergency. However, lack of antiviral drugs and vaccine against human coronaviruses demands a concerted approach to challenge the SARS-CoV-2 infection. Under limited resource and urgency, combinatorial computational approaches to identify the potential inhibitor from known drugs could be applied against risen COVID-19 pandemic. Thereof, this study attempted to purpose the potent inhibitors from the approved drug pool against SARS-CoV-2 main protease (M^pro). To circumvent the issue of lead compound from available drugs as antivirals, antibiotics with broad spectrum of viral activity, i.e. doxycycline, tetracycline, demeclocycline, and minocycline were chosen for molecular simulation analysis against native ligand N3 inhibitor in SARS-CoV-2 M^pro crystal structure. Molecular docking simulation predicted the docking score >−7 kcal/mol with significant intermolecular interaction at the catalytic dyad (His41 and Cys145) and other essential substrate binding residues of SARS-CoV-2 M^pro. The best ligand conformations were further studied for complex stability and intermolecular interaction profiling with respect to time under 100 ns classical molecular dynamics simulation, established the significant stability and interactions of selected antibiotics by comparison to N3 inhibitor. Based on combinatorial molecular simulation analysis, doxycycline and minocycline were selected as potent inhibitor against SARS-CoV-2 M^pro which can used in combinational therapy against SARS-CoV-2 infection.

Potential of NO donor furoxan as SARS-CoV-2 main protease (Mpro) inhibitors: in silico analysis

The sharp spurt in positive cases of novel coronavirus-19 (SARS-CoV-2) worldwide has created a big threat to human. In view to expedite new drug leads for COVID-19, Main Proteases (M^pro) of novel Coronavirus (SARS‐CoV‐2) has emerged as a crucial target for this virus. Nitric oxide (NO) inhibits the replication cycle of SARS-CoV. Inhalation of nitric oxide is used in the treatment of severe acute respiratory syndrome. Herein, we evaluated the phenyl furoxan, a well-known exogenous NO donor to identify the possible potent inhibitors through in silico studies such as molecular docking as per target analysis for candidates bound to substrate binding pocket of SARS-COV-2 M^pro. Molecular dynamics (MD) simulations of most stable docked complexes (M^pro-22 and M^pro-26) helped to confirm the notable conformational stability of these docked complexes under dynamic state. Furthermore, Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations revealed energetic contributions of key residues of M^pro in binding with potent furoxan derivatives 22, 26. In the present study to validate the molecular docking, MD simulation and MM-PBSA results, crystal structure of M^pro bound to experimentally known inhibitor X77 was used as control and the obtained results are presented herein. We envisaged that spiro-isoquinolino-piperidine-furoxan moieties can be used as effective ligand for SARS-CoV-2 M^pro inhibition due to the presence of key isoquinolino-piperidine skeleton with additional NO effect.

Communicated by Ramaswamy H. Sarma

KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules

MOTIVATION

The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process.

RESULTS

KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases.

AVAILABILITY AND IMPLEMENTATION

KinomeX is available at: https://kinome.dddc.ac.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico screening of known small molecules to bind ACE2 specific RBD on Spike glycoprotein of SARS-CoV-2 for repurposing against COVID-19

Background: Human coronavirus (SARS-CoV-2) is causing a pandemic with significant morbidity and mortality. As no effective novel drugs are available currently, drug repurposing is an alternative intervention strategy. Here we present an  in silico drug repurposing study that implements successful concepts of computer-aided drug design (CADD) technology for repurposing known drugs to interfere with viral cellular entry via the spike glycoprotein (SARS-CoV-2-S), which mediates host cell entry via the hACE2 receptor. Methods: A total of 4015 known and approved small molecules were screened for interaction with SARS-CoV-2-S through docking studies and 15 lead molecules were shortlisted. Additionally, streptomycin, ciprofloxacin, and glycyrrhizic acid (GA) were selected based on their reported anti-viral activity, safety, availability and affordability. The 18 molecules were subjected to molecular dynamics (MD) simulation. Results: The MD simulation results indicate that GA of plant origin may be repurposed for SARS-CoV-2 intervention, pending further studies. Conclusions: Repurposing is a beneficial strategy for treating COVID-19 with existing drugs. It is aimed at using docking studies to screen molecules for clinical application and investigating their efficacy in inhibiting SARS-CoV-2-S. SARS-CoV-2-S is a key pathogenic protein that mediates pathogen-host interaction. Hence, the molecules screened for inhibitory properties against SARS-CoV-2-S can be clinically used to treat COVID-19 since the safety profile is already known.

Investigation of Some Antiviral N-Heterocycles as COVID 19 Drug: Molecular Docking and DFT Calculations

The novel coronavirus, COVID-19, caused by SARS-CoV-2, is a global health pandemic that started in December 2019. The effective drug target among coronaviruses is the main protease M^pro, because of its essential role in processing the polyproteins that are translated from the viral RNA. In this study, the bioactivity of some selected heterocyclic drugs named Favipiravir (1), Amodiaquine (2), 2'-Fluoro-2'-deoxycytidine (3), and Ribavirin (4) was evaluated as inhibitors and nucleotide analogues for COVID-19 using computational modeling strategies. The density functional theory (DFT) calculations were performed to estimate the thermal parameters, dipole moment, polarizability, and molecular electrostatic potential of the present drugs; additionally, Mulliken atomic charges of the drugs as well as the chemical reactivity descriptors were investigated. The nominated drugs were docked on SARS-CoV-2 main protease (PDB: 6LU7) to evaluate the binding affinity of these drugs. Besides, the computations data of DFT the docking simulation studies was predicted that the Amodiaquine (2) has the least binding energy (-7.77 Kcal/mol) and might serve as a good inhibitor to SARS-CoV-2 comparable with the approved medicines, hydroxychloroquine, and remdesivir which have binding affinity -6.06 and -4.96 Kcal/mol, respectively. The high binding affinity of 2 was attributed to the presence of three hydrogen bonds along with different hydrophobic interactions between the drug and the critical amino acids residues of the receptor. Finally, the estimated molecular electrostatic potential results by DFT were used to illustrate the molecular docking findings. The DFT calculations showed that drug 2 has the highest of lying HOMO, electrophilicity index, basicity, and dipole moment. All these parameters could share with different extent to significantly affect the binding affinity of these drugs with the active protein sites.

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC50 of 7.2 μM. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Drug repositioning in cancer: The current situation in Japan

Cancer is a leading cause of death worldwide, and the incidence continues to increase. Despite major research aimed at discovering and developing novel and effective anticancer drugs, oncology drug development is a lengthy and costly process, with high attrition rates. Drug repositioning (DR, also referred to as drug repurposing), the process of finding new uses for approved noncancer drugs, has been gaining popularity in the past decade. DR has become a powerful alternative strategy for discovering and developing novel anticancer drug candidates from the existing approved drug space. Indeed, the availability of several large established libraries of clinical drugs and rapid advances in disease biology, genomics/transcriptomics/proteomics and bioinformatics has accelerated the pace of activity‐based, literature‐based and in silico DR, thereby improving safety and reducing costs. However, DR still faces financial obstacles in clinical trials, which could limit its practical use in the clinic. Here, we provide a brief review of DR in cancer and discuss difficulties in the development of DR for clinical use. Furthermore, we introduce some promising DR candidates for anticancer therapy in Japan.

Target identification among known drugs by deep learning from heterogeneous networks

Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug-gene-disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U.S. Food and Drug Administration-approved small molecule drugs, deepDTnet shows high accuracy (the area under the receiver operating characteristic curve = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published state-of-the-art methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC50 = 0.43 μM) of human retinoic-acid-receptor-related orphan receptor-gamma t (ROR-γt). Furthermore, by specifically targeting ROR-γt, topotecan reveals a potential therapeutic effect in a mouse model of multiple sclerosis. In summary, deepDTnet offers a powerful network-based deep learning methodology for target identification to accelerate drug repurposing and minimize the translational gap in drug development.