Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.

Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.

Protein Flexibility and Multiple Docking in Ligand Docking and Virtual Screening to the BRAF(TypeⅠ1/2)Inhibitors

BRAF has been recognized as a promising target for cancer therapy. A number of crystal structures have been published. Molecular docking is one of the most effective techniques in the field of computer-aided drug design(CADD). Appropriate protein conformation and docking method are essential for the successful virtual screening experiments. One approach considering protein flexibility and multiple docking methods was proposed in this study. Six DFG-in/αC-helix-out crystal structures of BRAF, three docking programs(Glide, GOLD and Ligand Fit) and 12 scoring functions were applied for the best combination by judging from the results of pose prediction and retrospective virtual screening(VS). The most accurate results(mean RMSD of about 0.6 ?) of pose prediction were obtained with two complex structures(PDB: 3 C4 C and 3 SKC) using Glide SP. From the retrospective VS, the most active compounds were identified by using the complex structure of 3 SKC, indicated by a ROC/AUC score of 0.998 and an EF of 20.6 at 5% of the database screen with Glide-SP. On the whole, PDB 3 SKC could achieve a higher rate of correct reproduction, a better enrichment and more diverse compounds. A comparison of 3 SKC and the other X-ray crystal structures led to a rationale for the docking results. PDB 3 SKC could achieve a broad range of sulfonamide substitutions through an expanded hydrophobic pocket formed by a further shift of the αC-helix. Our study emphasized the necessity and significance of protein flexibility and scoring functions in both ligand docking and virtual screening.

Pharmacophore Based Virtual Screening for Identification of Novel CDK₂Inhibitors

CDK2 is one of the most important members of Cyclin-dependent kinases. It is a critical modulator of various oncogenic signaling pathways, and its activity is vital for loss of proliferative control during oncogenesis. This work has focused on developing a pharmacophore model for CDK2 inhibitors by using a dataset of known inhibitors as a pre-filter throughout the virtual screening and docking process. Consequently, the best pharmacophore model was made of one hydrogen bond acceptor, and two aromatic ring features with a high correlation value of 0.906. The validation findings proved out that the selected model can be used as a filter to screen new molecules like Enamine kinase hinge region directed library against CDK2. As a result, 69 hits were subjected to molecular docking studies. Eventually, three compounds (5909, 701 and 8397) scored good interaction energy values and strong molecular interactions. Hence, they were identified as leads for novel CDK2 inhibitors as anticancer drugs.

Virtual Screening of Inhibitors for Chitosanases EAG1

Chitosanases EAG1 is a classical glycoside hydrolase from Bacillus ehimensis. The previous researches showed that this Chitosanases can not only hydrolyze the b1,4-glycosidic bonds of chitosan to COS in different sizes but also keep a high catalytic activity in organic, which was useful for producing chitooligosaccharides and GlcN for use in the food and pharmacological industries. While it is instable in the liquid state. This shortcoming seriously restricts its industrial application. Here we used the modeled structure of EAG1 and the molecular modeling software package to screen the free chemical database ZINC. Moreover, the strategies including “initial filter” and consensus scoring were applied to accelerate the process and improve the success rate of virtual screening. Finally, five compounds were screened and they were purchased or synthetized to test their binding affinity against EAG1. The test results showed that one of them could inhibit the enzyme with an apparent Ki of 1.5 μM. The result may take the foundation for further inhibitor screening and design against EAG1 and the screened compound may also help to improve the liquid stability of EAG1 and expand its industrial application.

Identification of Novel CDK9 Inhibitors with Better Inhibitory Activity and Higher Selectivity for Cancer Treatment by an Effective Two-Stage Virtual Screening Strategy

The aberrant overexpression of cyclin-dependent kinase 9 (CDK9) in cancer cells results in the loss of proliferative control, making it an attractive therapeutic target for various cancers. However, the highly structural similarity between CDK9 and CDK2 makes the development of novel selective CDK9 inhibitors a challenging task and thus limits their clinical applications. Here, an effective two-stage virtual screening strategy was developed to identify novel CDK9 inhibitors with better inhibitory activity and higher selectivity. The first screening stage aims to select potential compounds with better inhibitory activity than Roniciclib, one of the most effective CDK9 inhibitors, through reliable structure-based pharmacophoric virtual screening and accurate molecular docking analyses. The second stage employs a very detailed visual inspection process, in which several structural criteria describing the major difference between the binding pockets of CDK9 and CDK2 are taken into consideration, to identify compounds with higher selectivity than CAN508, one of the CDK9 inhibitors with distinguished selectivity. Finally, three compounds (NCI207113 from NCI database and TCM0004 and TCM3282 from TCM database) with better inhibitory activity and higher selectivity were successfully identified as novel CDK9 inhibitors. These three compounds also display excellent binding stabilities, great pharmacokinetic properties and low toxicity in MD simulations and ADMET predictions. Besides, the results of binding free energy calculations suggest that enhancing van der Waals interaction and nonpolar solvation energy and/or reducing polar solvation energy can significantly improve the binding affinity of these CDK9 inhibitors. Their clinical potentials to serve as anticancer drug candidates can be further evaluated through a series of in vitro/in vivo bioassays in the future. To the best of our knowledge, this is the first attempt to identify novel CDK9 inhibitors with both better inhibitory activity and higher selectivity through an effective two-stage virtual screening strategy.

Virtual Screening and Molecular Docking for Arylalkylamine-N-Acetyltransferase(aaNAT)Inhibitors,a Key Enzyme of Aedes(Stegomyia)aegypti(L.)Metabolism

Background: Dengue is a Neglected tropical disease (NTDs) with high incidence in Brazil. This disease is caused by Dengue virus and is transmitted by Aedes aegypti mosquito. The search for new approaches for controlling of this disease is the subject of numerous studies. The aaNAT is a key enzyme in the metabolism of A. aegypti and is crucial in the sclerotization process, as well as regulation of circadian rhythm and inactivation of neurotransmitters. Computational techniques applied to studies of biological systems become an effective weapon in the mapping and management of 3D data structures, giving direction and guidance of potential ligands that can form stable complexes with targets of interest, using a Molecular Docking approach. The present study was conducted by a virtual screening, followed by docking calculations, in order to find molecules that could inhibit aaNAT. In this study, we used available compounds in SAM database (Bioinformatics and Medicinal Chemistry Laboratory—Southwest Bahia State University, Jequié-Bahia, Brazil), PubChem and ZINC. Results: The result of dockings with selected ligands showed good energy affinities, presenting potential inhibitory interactions with the enzyme active site. Conclusions: The Coa-S-acetyl-tryptamine and 3-indoleacriloil-coenzyme-A showed the same binding energies -8.9 Kcal/Mol and were described as possible inhibitors of aaNAT.

Pharmacophore modeling and virtual screening for the discovery of new fatty acid amide hydrolase inhibitors

A predictive pharmacophore model has been generated from a series of diverse fatty acid amide hydrolase(FAAH)inhibitors and the optimal pharmacophore model applied in virtual screening.The pharmacophore model was based on a training set of 21 compounds carefully selected from the published literatures.The optimal model Hypo-1 included four features(two hydrogen-bond acceptor units,one aromatic hydrophobic unit and one aromatic ring unit)and two excluded volumes.Cross-validation of the model confirmed that Hypo-1 was not generated by chance correlation.A large test set of 55 compounds showed that Hypo-1 performed well in classifying highly active and less active FAAH inhibitors.Superimposition analysis of the FAAH X-ray crystal structure and the pharmacophore Hypo-1 further validated the adequacy of the model.Virtual screening generated a total of 976 hits from the Zinc Natural Products database,a hit rate of 1.04%and enrichment of 83.89.The acceptable hit rate further supports the use of Hypo-1 as a 3D query tool for virtual screening.

Design, synthesis and biological evaluation of novel histone deacetylase inhibitors based on virtual screening

Ligand-and structure-based virtual screening methods were employed to identify novel non-hydroxamate histone deacetylase(HDAC)inhibitors.Based on the newly identified hit compound 17a,three series of compounds were synthesized and evaluated for both HDAC1 inhibitory activity and cytotoxicity.Binding modes of representative structures were analyzed using the docking method to explain the observed disparity in HDAC1 inhibitory activities.

Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs

The pandemic of novel coronavirus disease 2019(COVID-19)has rampaged the world,with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by 23 November 2020.There is an urgent need to identify effective drugs and vaccines to fight against the virus.Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)belongs to the family of coronaviruses consisting of four structural and 16 non-structural proteins(NSP).Three non-structural proteins,main protease(Mpro),papain-like protease(PLpro),and RNAdependent RNA polymerase(RdRp),are believed to have a crucial role in replication of the virus.We applied computational ligand-receptor binding modeling and performed comprehensive virtual screening on FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina,Glide,and rDock.Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2,including antiemetics rolapitant and ondansetron for Mpro;labetalol and levomefolic acid for PLpro;and leucal and antifungal natamycin for RdRp.Molecular dynamics simulation confirmed the stability of the ligand-protein complexes.The results of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy(low inhibitory effect)with all three proteins—Mpro,PLpro,and RdRp.In summary,our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.

Discovery of Potential SARS-CoV-2M Protease Inhibitors by Virtual Screening,Molecular Dynamics,and Binding Free Energy Analyses

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)gained tremendous attention due to its high infectivity and pathogenicity.The 3-chymotrypsin-like hydrolase protease(Mpro)of SARS-CoV-2 has been proven to be an important target for anti-SARS-CoV-2 activity.To better identify the drugs with potential in treating coronavirus disease 2019(COVID-19)caused by SARS-CoV-2 and according to the crystal structure of Mpro,we conducted a virtual screening of FDA-approved drugs and chemical agents that have entered clinical trials.As a result,9 drug candidates with therapeutic potential for the treatment of COVID-19 and with good docking scores were identified to target SARS-CoV-2.Consequently,molecular dynamics(MD)simulation was performed to explore the dynamic interactions between the predicted drugs and Mpro.The binding mode during MD simulation showed that hydrogen bonding and hydrophobic interactions played an important role in the binding processes.Based on the binding free energy calculated by using MM/PBSA,Lopiravir,an inhibitor of human immunodeficiency virus(HIV)protease,is under investigation for the treatment of COVID-19 in combination with ritionavir,and it might inhibit Mpro effectively.Moreover,Ombitasvir,an inhibitor for non-structural protein 5 A of hepatitis C virus(HCV),has good inhibitory potency for Mpro.It is notable that the GS-6620 has a binding free energy,with respect to binding Mpro,comparable to that of ombitasvir.Our study suggests that ombitasvir and lopinavir are good drug candidates for the treatment of COVID-19,and that GS-6620 has good anti-SARS-CoV-2 activity.

Study of Aldo-keto Reductase 1C3 Inhibitor with Novel Framework for Treating Leukaemia Based on Virtual Screening and In vitro Biological Activity Testing

Aldo-keto reductase 1C3(AKR1C3)is a potential target for the treatment of acute myeloid leukaemia and T-cell acute lymphoblastic leukaemia.In this study,pharmacophore models,molecular docking and virtual screening of target prediction were used to find a potential AKR1C3 inhibitor.Firstly,eight bacteriocin derivatives(Z1-Z8)were selected as training sets to construct 20 pharmacophore models.The best pharmacophore model MODEL_016 was obtained by Decoy test(the enrichment degree was 21.5117,and the fitting optimisation degree was 0.9668).Secondly,MODEL_016 was used for the virtual screening of ZINC database.Thirdly,the hit 83256 molecules were docked into the AKR1C3 protein.Compared to the total scores and interactions between compounds and protein,16532 candidate compounds with higher docking scores and interactions with important residues PHE306 and TRP227 were screened.Lastly,eight compounds(A1-A8)that had good absorption,distribution,metabolism,excretion and toxicity(ADMET)properties were obtained by target prediction.Compounds A3 and A7 with high total score and good target prediction results were selected for in vitro biological activity test,whose IC_(50) values were 268.3 and 88.94µmol/L,respectively.The results provide an important foundation for the discovery of novel AKR1C3 inhibitors.The research methods used in this study can also provide important references for the research and development of new drugs.

1,5-O-Dicaffeoyl-quinic Acid as a Novel Potential NMDA Receptor Inhibitor from Traditional Chinese Medicine Database by Virtual Screening

Objective Neurodegenerative diseases, such as ischemia, traumatic injury, Alzheimer's disease, and Parkinson's disease are characterized by neuronal loss and dysfunction. It is known that glutamate-induced toxicity plays an important role in neurodegenerative diseases. Glutamate toxicity seems to be mediated by excessive influx of Ca^(2+) into neuronal cells through activation of N-methyl-D-aspartate(NMDA) receptor. To search for potential NMDA receptor inhibitors in traditional Chinese medicine. Methods A series of computer methods including drug-likeness evaluation, ADMET tests as well as molecular docking have been used. Results 1,5-O-dicaffeoyl-quinic acid was identified as NMDA receptor inhibitor by virtual screening. Its neuroprotective activity was further confirmed by in vitro test. 1,5-O-dicaffeoyl-quinic acid showed strong neuroprotection against NMDA-induced cell injury. Conclusion 1,5-O-Dicaffeoylquinic acid may be regarded as a potential NMDA receptor inhibitor for the prevention and treatment of neurodegenerative disorders.

Eugenol targeting CrtM inhibits the biosynthesis of staphyloxanthin in Staphylococcus aureus

Staphylococcus aureus is a serious foodborne pathogen threatening food safety and public health.Especially the emergence of methicillin-resistant Staphylococcus aureus(MRSA)increased the difficulty of S.aureus treatment.Staphyloxanthin is a crucial virulence factor of S.aureus.Blocking staphyloxanthin production could help the host immune system counteract the invading S.aureus cells.In this study,we first screened for staphyloxanthin inhibitors using a virtual screening method.The outcome of the virtual screening method resulted in the identification of eugenol(300μg/mL),which significantly inhibits the staphyloxanthin production in S.aureus ATCC 29213,S.aureus Newman,MRSA ATCC 43300 and MRSA ATCC BAA1717by 84.2%,63.5%,68.1%,and 79.5%,respectively.The outcome of the growth curve assay,field-emission scanning electron,and confocal laser scanning microscopy analyses confirmed that eugenol at the test concentration did not affect the morphology and growth of S.aureus.Moreover,the survival rate of S.aureus ATCC 29213 and MRSA ATCC 43300 under H_(2)O_(2) pressure decreased to 51.9%and 45.5%in the presence of eugenol,respectively.The quantitative RT-PCR and molecular simulation studies revealed that eugenol targets staphyloxanthin biosynthesis by downregulating the transcription of the crtM gene and inhibiting the activity of the CrtM enzyme.Taken together,we first determined that eugenol was a prominent compound for staphyloxanthin inhibitor to combat S.aureus especially MRSA infections.

Exploration of cyclooxygenase-2 inhibitory peptides from walnut dreg proteins based on in silico and in vitro analysis

Walnut dreg protein hydrolysates(WDPHs)exhibit a variety of biological activities,however,the cyclooxygenase-2(COX-2)inhibitory peptide of WDPHs remain unclear.The aim of this study was to rapidly screen for such peptides in WDPHs through a combination of in silico and in vitro analysis.In total,1262 peptide sequences were observed by nano liquid chromatography/tandem mass spectrometry(nano LC-MS/MS)and 4 novel COX-2 inhibitory peptides(AGFP,FPGA,LFPD,and VGFP)were identified.Enzyme kinetic data indicated that AGFP,FPGA,and LFPD displayed mixed-type COX-2 inhibition,whereas VGFP was a non-competitive inhibitor.This is mainly because the peptides form hydrogen bonds and hydrophobic interactions with residues in the COX-2 active site.These results demonstrate that computer analysis combined with in vitro evaluation allows for rapid screening of COX-2 inhibitory peptides in walnut protein dregs.

Functional analysis and screening small molecules to RpfF protein in Xanthomonas oryzae involved in rice bacterial blight disease

Xanthomonas oryzae pv.oryzae(Xoo) is an important rice pathogen.This is a vascular pathogen entering the plant via the hydathodes causing rice bacterial blight.It has been known that most regulation of pathogenicity factor F(RpfF) genes in Xanthomonas regulates virulence in response to the diffusible signal factor(DSF).The RpfF recognized as an attractive drug target in bacterial rice blight disease.In this study,we performed the gene-gene interaction of RpfF and pathway functional analysis.3 D structure of RpfF protein was predicted using a homology modelling tool Swiss-Model and refined by molecular dynamics(MD) simulation.The refined model protein was predicted structural assessment using various tools such as PROCHECK,ERRAT,and VERIFY-3 D.We have collected 2 500 rifampicin analogues from Zinc Database by virtual screening.The screened compounds were docked into the active site of the RpfF protein using AutoDock Vina in PyRx Virtual Screening Tool.Furthermore,docking result and in silico ADMET analysis described that the compounds ZINC03056414,ZINC03205310,ZINC08673779,ZINC09100848,ZINC09729566,ZINC11415953,ZINC12810788,ZINC24989313,ZINC27441787 and ZINC32739565 have best binding energies and less toxicity than reference compound.This study revealed that the active site residues such as HIS-118,HIS-147,THR-148,ARG-179,ASP-207,ARG-240 and THR-244 are key roles in the pathogenicity.It could be beneficial in the design of small molecule therapeutics or the treatment of rice bacterial blight disease.

Dihydroergotamine ameliorates liver fibrosis by targeting transforming growth factor β type Ⅱ receptor

BACKGROUND The transforming growth factor β(TGFβ) signaling pathway plays a crucial role in the development of liver fibrosis by activating TGFβ type Ⅱ receptor(TGFβR2), followed by the recruitment of TGFβR1 finally triggering downstream signaling pathway.AIM To find drugs targeting TGFβR2 that inhibit TGFβR1/TGFβR2 complex formation, theoretically inhibit TGFβ signaling pathway, and thereby ameliorate liver fibrosis.METHODS Food and Drug Administration-approved drugs were screened for binding affinity with TGFβR2 by virtual molecular docking. We identified 6 candidates and further explored their potential by Cell Counting Kit-8(CCK-8) cell cytotoxic experiment to validate toxicity and titrated the best cellular working concentrations. Next, we further demonstrated the detailed molecular working mechanisms using mutagenesis analysis. Finally, we used a mouse model to investigate its potential anti-liver fibrosis effect.RESULTS We identified 6 drug candidates. Among these 6 drugs, dihydroergotamine(DHE) shows great ability in reducing fibrotic gene expressions such as collagen, p-SMAD3, and α-SMA in TGFβ induced cellular model of liver fibrosis in LX-2 cells. Furthermore, we demonstrated that DHE binds to TGFβR2. Moreover, mutation of Leu27, Phe30, Thr51, Ser52, Ile53, and Glu55 of TGFβR2 disrupted the binding of TGFβR2 with DHE. In addition, DHE significantly improved liver fibrosis, as evidenced by Masson’s trichrome staining of liver sections. This is further supported by the width and the velocity of the portal vein, and serum markers of liver function. In line with those observations, DHE also decreased macrophages infiltration and extracellular matrix deposition in the liver.CONCLUSION DHE alleviates liver fibrosis by binding to TGFβR2 thereby suppressing TGFβ signaling pathway. We show here that as far as drug repurposing, DHE has great potential to treat liver fibrosis.

Discovery of human coronaviruses pan-papain-like protease inhibitors using computational approaches

The papain-like protease(PLpro)is vital for the replication of coronaviruses(Co Vs),as well as for escaping innate-immune responses of the host.Hence,it has emerged as an attractive antiviral drug-target.In this study,computational approaches were employed,mainly the structure-based virtual screening coupled with all-atom molecular dynamics(MD)simulations to computationally identify specific inhibitors of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)PLpro,which can be further developed as potential pan-PLprobased broad-spectrum antiviral drugs.The sequence,structure,and functional conserveness of most deadly human Co Vs PLprowere explored,and it was revealed that functionally important catalytic triad residues are well conserved among SARS-Co V,SARS-Co V-2,and middle east respiratory syndrome coronavirus(MERS-Co V).The subsequent screening of a focused protease inhibitors database composed of^7,000 compounds resulted in the identification of three candidate compounds,ADM13083841,LMG15521745,and SYN15517940.These three compounds established conserved interactions which were further explored through MD simulations,free energy calculations,and residual energy contribution estimated by MM-PB(GB)SA method.All these compounds showed stable conformation and interacted well with the active residues of SARS-Co V-2 PLpro,and showed consistent interaction profile with SARS-Co V PLproand MERS-Co V PLproas well.Conclusively,the reported SARS-Co V-2 PLprospecific compounds could serve as seeds for developing potent pan-PLprobased broad-spectrum antiviral drugs against deadly human coronaviruses.Moreover,the presented information related to binding site residual energy contribution could lead to further optimization of these compounds.

Drug repurposing against coronavirus disease 2019(COVID-19):A review

Since December 2019,severe acute respiratory syndrome coronavirus 2 has been found to be the culprit in the coronavirus disease 2019(COVID-19),causing a global pandemic.Despite the existence of many vaccine programs,the number of confirmed cases and fatalities due to COVID-19 is still increasing.Furthermore,a number of variants have been reported.Because of the absence of approved anticoronavirus drugs,the treatment and management of COVID-19 has become a global challenge.Under these circumstances,drug repurposing is an effective method to identify candidate drugs with a shorter cycle of clinical trials.Here,we summarize the current status of the application of drug repurposing in COVID-19,including drug repurposing based on virtual computer screening,network pharmacology,and bioactivity,which may be a beneficial COVID-19 treatment.

Repurposing of FDA-Approved drugs to predict new inhibitors against key regulatory genes in Mycobacterium tuberculosis

Tuberculosis(TB)disease has become one of the major public health concerns globally,especially in developing countries.Numerous research studies have already been carried out for TB,but we are still struggling for a complete and quick cure for it.The progress of Mycobacterium tuberculosis(MTB)strains resistant to existing drugs makes its cure and control very complicated.Therefore,it is the need of the hour to search for newer and effective drugs that can inhibit an increasing number of putative drug targets.We applied the drug repurposing concept to identify promising FDAapproved drugs against five key-regulatory genes(FurB,IdeR,KstR,MosR,and RegX3)of the MTB.The FDA drugs were virtually screened using a structure-based approach by GOLD versions 5.2,and subjected to rigid docking followed by an induced-fit docking algorithm to enhance the accuracy and prioritize drugs for repurposing.We found 11 candidate drugs(including ZINC03871613,ZINC03871614,ZINC03871615 as top scorer candidate drugs)that were frequently present within the top 20 GoldScore ranks and showed promising results.Furthermore,molecular dynamics simulation was performed to monitor the effect of the top scorer drugs on the structural stability of all the five targets,indicating that inhibitors preferentially bind to the active site of the targets.This work suggests that these known FDA-approved drugs open new application domains in the form of anti-tuberculosis agents.