Quantifying nasal deformities using a novel mathematical method to complement preoperative assessment in rhinoplasty patients.

BACKGROUND: Rhinoplasty enhances facial symmetry and functionality. However, the accurate and reliable quantification of nasal defects pre-surgery remains an ongoing challenge. AIM: This study introduces a novel approach for defect quantification using 2D images and artificial intelligence, providing a tool for better preoperative planning and improved surgical outcomes. MATERIALS AND METHODS: A pre-trained AI model for facial landmark detection was utilised on a dataset of 250 images of male patients aged 18 to 24 who underwent rhinoplasty for cosmetic nasal deformity correction. The analysis concentrated on 36 different distances between the facial landmarks. These distances were normalised using min-max scaling to counter image size and quality variations. Post-normalisation, statistical parameters, including mean, median, and standard deviation, were calculated to identify and quantify nasal defects. RESULTS: The methodology was tested and validated using images from different ethnicities and regions, showing promising potential as a beneficial surgical aid. The normalised data produced reliable quantifications of nasal defects (average 76.2%), aiding in preoperative planning and improving surgical outcomes and patient satisfaction. APPLICATIONS: The developed method can be extended to other facial plastic surgeries. Furthermore, it can be used to create app-based software, assist medical education, and improve patient-doctor communication. CONCLUSION: This novel method for defect quantification in rhinoplasty using AI and image processing holds significant potential in improving surgical planning, outcomes, and patient satisfaction, marking an essential step in the fusion of AI and plastic surgery.

New insights into APCVD grown monolayer MoS2 using time-domain terahertz spectroscopy.

In modern era, wireless communications at ultrafast speed are need of the hour and search for its solution through cutting edge sciences is a new perspective. To address this issue, the data rates in order of terabits per second (TBPS) could be a key step for the realization of emerging sixth generation (6G) networks utilizing terahertz (THz) frequency regime. In this context, new class of transition metal dichalcogenides (TMDs) have been introduced as potential candidates for future generation wireless THz technology. Herein, a strategy has been adopted to synthesize high-quality monolayer of molybdenum di-sulfide (MoS2) using indigenously developed atmospheric pressure chemical vapor deposition (APCVD) set-up. Further, the time-domain transmission and sheet conductivity were studied as well as a plausible mechanism of terahertz response for monolayer MoS2 has been proposed and compared with bulk MoS2. Hence, the obtained results set a stepping stone to employ the monolayer MoS2 as potential quantum materials benefitting the next generation terahertz communication devices.

Pr[m]: An Algorithm for Protein Motif Discovery.

Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m].

Comparative modeling and structure based drug repurposing of PAX2 transcription factor for targeting acquired chemoresistance in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modeled, and the response of key amino acids to perturbation was identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of -9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches.Communicated by Ramaswamy H. Sarma.

Hypothetical Proteins as Predecessors of Long Non-coding RNAs.

Hypothetical Proteins [HP] are the transcripts predicted to be expressed in an organism, but no evidence of it exists in gene banks. On the other hand, long non-coding RNAs [lncRNAs] are the transcripts that might be present in the 5' UTR or intergenic regions of the genes whose lengths are above 200 bases. With the known unknown [KU] regions in the genomes rapidly existing in gene banks, there is a need to understand the role of open reading frames in the context of annotation. In this commentary, we emphasize that HPs could indeed be the predecessors of lncRNAs.

Author Correction: Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent.

Drug discovery for a vigorous and feasible lead candidate is a challenging scientific mission as it requires expertise, experience, and huge investment. Natural products and their derivatives having structural diversity are renowned source of therapeutic agents since many years. Tyrosol (a natural phenylethanoid) has been extracted from olive oil, and its structure was confirmed by elemental analysis, FT-IR, FT-NMR, and single crystal X-ray crystallography. The conformational analysis for tyrosol geometry was performed by Gaussian 09 in terms of density functional theory. Validation of bond lengths and bond angles obtained experimentally as well as theoretically were performed with the help of curve fitting analysis, and values of correlation coefficient (R) obtained as 0.988 and 0.984, respectively. The charge transfer within the tyrosol molecule was confirmed by analysis of HOMO→LUMO molecular orbitals. In molecular docking with COX-2 (PDB ID: 5F1A), tyrosol was found to possess satisfactory binding affinity as compared to other NSAIDs (Aspirin, Ibuprofen, and Naproxen) and a COX-2 selective drug (Celecoxib). ADMET prediction, drug-likeness and bioactivity score altogether confirm the lead/drug like potential of tyrosol. Further investigation of simulation quality plot, RMSD and RMSF plots, ligands behavior plot as well as post simulation analysis manifest the consistency of 5F1A-tyrosol complex throughout the 20 ns molecular simulation process that signifies its compactness and stability within the receptor pocket. AbbreviationsADMETAbsorption, Distribution, Metabolism, Excretion and ToxicityÅAngstromCOX-2Cyclooxygenase-2DFTDensity Functional TheoryDMFDimethylformamideFMOFrontier Molecular OrbitalFT-IRFourier-transform Infrared SpectroscopyFT-NMRNuclear Magnetic Resonance SpectroscopyHOMOHighest Occupied Molecular OrbitalLUMOLowest Unoccupied Molecular OrbitalMDMolecular DynamicsNSNanosecondNSAIDsNon-steroidal anti-inflammatory drugsOPEOsiris Property ExplorerRMSDRoot-Mean-Square DeviationRMSFRoot Sean Square FluctuationCommunicated by Ramaswamy H. Sarma.

Structure-based drug designing and identification of Woodfordia fruticosa inhibitors targeted against heat shock protein (HSP70-1) as suppressor for Imiquimod-induced psoriasis like skin inflammation in mice model.

Heat shock proteins (HSPs) emerged as a therapeutic target and it was observed that inhibition of HSP70-1 plays a pivotal role in the management of psoriasis. In-silico investigation involving techniques like molecular docking and molecular dynamics (MD) simulation analysis was performed against HSP70-1. Further, anti-psoriatic activity of bioactive immunomodulatory compounds present in ethanolic extract of Woodfordia fruticosa flowers (Wffe) using combination of bioinformatics together with ethnopharmacological approach has been explored in this study. Myricetin (-8.024), Quercetin (-7.368) and Ellagic acid (-7.311) were the top three compounds with minimum energy levels as well as high therapeutic value/ADMET as compared to currently available marketed anti-psoriatic drug Tretinoin (-7.195). ADMET prediction was used to screen ligands for drug-likeness and efficacy. Further, biogenically Woodfordia fruticosa gold nanoparticles (WfAuNPs) were synthesized and characterized by UV-Visible Spectroscopy (UV-vis), Dynamic Light Scattering (DLS), Zeta Potential, X-Ray Diffraction (XRD) and High Resolution Transmission Electron Microscopy (HRTEM) techniques. Synthesized WfAuNPs observed in the size range of 10-20 nm and were used to develop WfAuNPs-Carbopol®934 ointment gel. Subsequently, the therapeutic efficacy of WfAuNPs-Carbopol® 934 was checked against 5% Imiquimod-induced psoriasis like skin inflammation. WfAuNPs-Carbopol® 934 was found to be exerting better therapeutic effect in reducing the mean DAI score (0.63 ±â€¯0.08), serum cytokines (TNF-α, IL-22 and IL-23) levels along with reduced epidermal thickness, parakeratosis and marked decrease in the hyperproliferation of keratinocytes. Results of the study revealed that the WfAuNPs-Carbopol® 934 could be an effective alternative treatment for psoriasis in near future.

Computational and In-Vitro Validation of Natural Molecules as Potential Acetylcholinesterase Inhibitors and Neuroprotective Agents.

BACKGROUND: Cholinesterase inhibitors are the first line of therapy for the management of Alzheimer's disease (AD), however, it is now established that they provide only temporary and symptomatic relief, besides, having several inherited side-effects. Therefore, an alternative drug discovery method is used to identify new and safer 'disease-modifying drugs'. METHODS: Herein, we screened 646 small molecules of natural origin having reported pharmacological and functional values through in-silico docking studies to predict safer neuromodulatory molecules with potential to modulate acetylcholine metabolism. Further, the potential of the predicted molecules to inhibit acetylcholinesterase (AChE) activity and their ability to protect neurons from degeneration was determined through in-vitro assays. RESULTS: Based on in-silico AChE interaction studies, we predicted quercetin, caffeine, ascorbic acid and gallic acid to be potential AChE inhibitors. We confirmed the AChE inhibitory potential of these molecules through in-vitro AChE inhibition assay and compared results with donepezil and begacestat. Herbal molecules significantly inhibited enzyme activity and inhibition for quercetin and caffeine did not show any significant difference from donepezil. Further, the tested molecules did not show any neurotoxicity against primary (E18) hippocampal neurons. We observed that quercetin and caffeine significantly improved neuronal survival and efficiently protected hippocampal neurons from HgCl2 induced neurodegeneration, which other molecules, including donepezil and begacestat, failed to do. CONCLUSION: Quercetin and caffeine have the potential as "disease-modifying drugs" and may find application in the management of neurological disorders such as AD.

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach.

BACKGROUND: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells. OBJECTIVE: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks. METHODOLOGY: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy. RESULTS: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network. CONCLUSION: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Systems Biology: A Powerful Tool for Drug Development.

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.

Drug Target Interplay: A Network-based Analysis of Human Diseases and the Drug Targets.

Screening and identifying a disease-specific novel drug target is the first step towards a rational drug designing approach. Due to the advent of high throughput data generation techniques, the protein search space has now exceeded 24,500 human protein coding genes, which encodes approximately 1804proteins. This work aims at mining out the relationship between target proteins, drugs, and diseases genes through a network-based systems biology approach. A network of all FDA approved drugs, along with their targets were utilized to construct the proposed Drug Target (DT) network. Further, the experimental drugs were mapped into the DT network to infer the functional relationship by utilizing the respective network attributes. Similar to the DT network, a network of disease genes was created through OMIM Gene Map and Morbid Map, to link the binary associations of disorder-disease genes. In the proposed model of Human Interactome Network, shortest path length between the target protein and disease gene was used to infer the correlation between 'Drug Targets' and 'Disease-Gene'. This network-based study will help researchers to analyze, infer and identify disease-specific novel drug targets through harnessing the graph theory based network attributes.

Identification of Novel Abiotic Stress Proteins in Triticum aestivum Through Functional Annotation of Hypothetical Proteins.

Cereal grain bread wheat (T. aestivum) is an important source of food and belongs to Poaceae family. Hypothetical proteins (HPs), i.e., proteins with unknown functions, share a substantial portion of wheat proteomes and play important roles in growth and physiology of plant system. Several functional annotations studies utilizing the protein sequences for characterization of role of individual protein in physiology of plant systems were being reported in recent past. In this study, an integrated pipeline of software/servers has been used for the identification and functional annotation of 124 unique HPs of T. aestivum considering available data in NCBI till date. All HPs were broadly annotated, out of which functions of 77 HPs were successfully assigned with high confidence level. Precisely functional annotation of remaining 47 HPs is also characterized with low confidence. Several latest versions of protein family databases, pathways information, genomics context methods and in silico tools were utilized to identify and assign function for individual HPs. Annotation result of several HPs mainly belongs to cellular protein, metabolic enzymes, binding proteins, transmembrane proteins, transcription factors and photosystem regulator proteins. Subsequently, functional analysis has revealed the role of few HPs in abiotic stress, which were further verified by phylogenetic analysis. The functionally associated proteins with each of above-mentioned abiotic stress-related proteins were identified through protein-protein interaction network analysis. The outcome of this study may be helpful for formulating general set pipeline/protocols for a better understanding of the role of HPs in physiological development of various plant systems.

Transcriptomic Analysis of Soil Grown T. aestivum cv. Root to Reveal the Changes in Expression of Genes in Response to Multiple Nutrients Deficiency.

Deficiency of necessary macronutrients, i.e., Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), and Sulfate (S) in the soil leads to a reduction in plant growth and yield, which is a result of changes in expression level of various genes. This study was performed to identify the differentially expressed genes and its associated metabolic pathways occurred in soil grown wheat root samples excavated from the control and treated fields. To identify the difference in gene expression levels due to deficiency of the said nutrients, a transcriptomic, meta-analysis was performed on array expression profile data. A set of 435 statistically significant probes encoding 398 Nutrient Deficiency Response Genes (NRGs) responding at-least one nutrients deficiency (ND) were identified. Out of them 55 NRGs were found to response to minimum two ND. Singular Enrichment Analysis (SEA) predicts ontological based classifications and functional analysis of NRGs in different cellular/molecular pathways involved in root development and growth. Functional annotation and reaction mechanism of differentially expressed genes, proteins/enzymes in the different metabolic pathway through MapMan analysis were explored. Further the meta-analysis was performed to revels the active involvement each NRGs in distinct tissues and their comparative potential expression analysis in different stress conditions. The study results in exploring the role of major acting candidate genes such as Non-specific serine/threonine protein kinase, Xyloglucan endotransglucosylase/hydrolase, Peroxides, Glycerophosphoryl diester phosphodiesterase, S-adenosylmethionine decarboxylase proenzyme, Dehydrin family proteins, Transcription factors, Membrane Proteins, Metal binding proteins, Photosystem proteins, Transporter and Transferase associated in different metabolic pathways. Finally, the differences of transcriptional responses in the soil-grown root of T. aestivum cv. and in-vitro grown model plants under nutrients deficiency were summarized.

Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis.

Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is required. The application of deep transcriptional sequencing has been found to be reported to provide an efficient genomic assay to delve into the insights of the diseases and may prove to be useful in the study of Breast cancer. In this study, ChIP-Seq data for normal samples and Breast cancer were compared, and differential peaks identified, based upon fold enrichment (with P-values obtained via t-tests). The Protein-protein interaction (PPI) network analysis was carried out, following which the highly connected genes were screened and studied, and the most promising ones were selected. Biological pathway involved in the process were then identified. Our findings regarding potential Breast cancer-related genes enhances the understanding of the disease and provides prognostic information in addition to standard tumor prognostic factors for future research.

Molecular docking and dynamics simulation study of flavonoids as BET bromodomain inhibitors.

Bromodomains (BRDs) are the epigenetic proteins responsible for transcriptional regulation through its interaction with methylated or acetylated histone residues. The lysine residues of Bromodomain-1 (BD1) of Brd4 undergo ε-N-Acetylation posttranslational modifications to control transcription of genes. Due to its role in diverse cellular functions, Brd4 of bromodomain family, was considered as a prominent target for many diseases such as cancer, obesity, kidney disease, lung fibrosis, inflammatory diseases, etc. In this study, an attempt has been made to screen compounds from flavonoids and extended flavonoids libraries targeting acetylated lysine (KAc) binding site of BD1 of Brd4 using docking and molecular dynamics simulations. Two different docking programs AutoDock and Glide were used to compare their suitability for the receptor. Interestingly, in both the docking programs, the screened flavonoids have occupied the same binding pocket confirming the selection of active site. Further the MMGBSA binding free energy calculations and ADME analysis were carried out on screened compounds to establish their anti-cancerous properties. We have identified a flavonoid which shows docking and Glide e-model score comparatively much higher than those of already reported known inhibitors against Brd4. The protein-ligand complex with top-ranked flavonoid was used for dynamics simulation study for 50 ns in order to validate its stability inside the active site of Brd4 receptor. The results provide valuable information for structure-based drug design of Brd4 inhibitors.

Structural insights into conformational stability of both wild-type and mutant EZH2 receptor.

Polycomb group (PcG) proteins have been observed to maintain the pattern of histone by methylation of the histone tail responsible for the gene expression in various cellular processes, of which enhancer of zeste homolog 2 (EZH2) acts as tumor suppressor. Overexpression of EZH2 results in hyper activation found in a variety of cancer. Point mutation on two important residues were induced and the results were compared between the wild type and mutant EZH2. The mutation of Y641 and A677 present in the active region of the protein alters the interaction of the top ranked compound with the newly modeled binding groove of the SET domain, giving a GLIDE score of -12.26 kcal/mol, better than that of the wild type at -11.664 kcal/mol. In depth analysis were carried out for understanding the underlying molecular mechanism using techniques viz. molecular dynamics, principal component analysis, residue interaction network and free energy landscape analysis, which showed that the mutated residues changed the overall conformation of the system along with the residue-residue interaction network. The insight from this study could be of great relevance while designing new compounds for EZH2 enzyme inhibition and the effect of mutation on the overall binding mechanism of the system.

Antiviral therapy with nucleotide/nucleoside analogues in chronic hepatitis B: A meta-analysis of prospective randomized trials.

Nucleotide/nucleoside analogues (antiviral therapy) are used in the therapy of HBeAg positive and HBeAg negative chronic hepatitis B. We analyzed ten selected randomized controlled with 2557 patients to estimate the effect of antiviral drugs in chronic hepatitis B with compared to placebo. Virological response, biochemical response, histological response, seroconversion of HBeAg, and loss of HBeAg were estimated as primary efficacy measures. The included studies were subjected for heterogeneity and publication bias. The heterogeneity was assessed with χ2 and I(2) statistics. Publication bias was assessed by funnel plot. Greater rates of improvement obtained in antiviral group for virological response [43.96 % vs. 3.15 %, RR = 0.57, 95 % CI = 0.54-0.61, p-value <0.00001], biochemical response [58.37 % vs. 21.87 %, RR = 0.52, 95 % CI = 0.48-0.56, p-value <0.00001], histological response [58.99 % vs. 27.13 %, RR = 0.56, 95 % CI = 0.50-0.63, p-value <0.0001], seroconversion of HBeAg [10.66 % vs. 5.56 %, RR = 0.94, 95 % CI = 0.91-0.97, p-value = 0.0005], and HBeAg loss [14.59 % vs. 9.64 %, RR = 0.92, 95 % CI = 0.88-0.96, p-value = 0.0002]. The safety analysis were carried out for adverse events such as headache [17.22 % vs. 17.34 %, OR = 1.09, 95 % CI = 0.81-1.46, p-value = 0.58], abdominal pain [16.46 % vs. 14.34 %, OR = 1.24, 95 % CI = 0.90-1.72, p-value = 0.19], and pharyngitis [22.22 % vs. 18.23 %, OR = 1.12, 95 % CI = 0.86-1.45, p-value = 0.40]. Excluding adverse events, all primary efficacy measures shown statistical significant result for chronic hepatitis treatment (p-value <0.05). Antiviral therapy provided significant benefit for the treatment of chronic hepatitis B with no measurable adverse effects.

Effect of STAT3 inhibitor in chronic myeloid leukemia associated signaling pathway: a mathematical modeling, simulation and systems biology study.

Chronic myeloid leukemia (CML) is a hematopoietic stem-cell disorder which proliferates due to abnormal growth of basophil cells. Several proangiogenic molecules have been reported to be associated in CML progression, including the hepatocyte growth factor (HGF). However, detail mechanism about the cellular distribution and function of HGF in CML is yet to be revealed. The proliferation of hematopoietic cells are regulated by some of the growth factors like interleukin 3 (IL-3), IL-6, erythropoietin, thrombopoietin, etc. In this study IL-6 pathways have been taken into consideration which induces JAK/STAT and MAPK pathways to decipher the CML progression stages. An attempt has been made to model these pathways with the help of ordinary differential equations (ODEs) and estimating unknown parameters through fminsearch optimization algorithm. Some of the specific component like STAT3, of the pathway has been analyzed in detail and their role in CML progression has been elucidated. The roles of STAT3 inhibitors into the treatment of CML have been thoroughly studied and optimum concentration of the inhibitors have been predicted.

Flavonoids as Multi-target Inhibitors for Proteins Associated with Ebola Virus: In Silico Discovery Using Virtual Screening and Molecular Docking Studies.

Ebola virus is a single-stranded, negative-sense RNA virus that causes severe hemorrhagic fever in humans and non-human primates. This virus is unreceptive to a large portion of the known antiviral drugs, and there is no valid treatment as on date for disease created by this pathogen. Looking into its ability to create a pandemic scenario across globe, there is an utmost need for new drugs and therapy to combat this life-threatening infection. The current study deals with the evaluation of the inhibitory activity of flavonoids against the four selected Ebola virus receptor proteins, using in silico studies. The viral proteins VP40, VP35, VP30 and VP24 were docked with small molecules obtained from flavonoid class and its derivatives and evaluated on the basis of energetics, stereochemical considerations and pharmacokinetic properties to identify potential lead compounds. The results showed that both top-ranking screened flavonoids, i.e., Gossypetin and Taxifolin, showed better docking scores and binding energies in all the EBOV receptors when compared to those of the reported compound. All the screened flavonoids have known antiviral activity, acceptable pharmacokinetic properties and are being used on human and thus can be taken as anti-Ebola therapy without the time lag for clinical trial.