Computer-aided Study Research Articles

Experiments and Calculation on New N,N-bis-Tetrahydroacridines.

Tetrahydroacridines arouse particular interest due to the potential possibilities of application in the medical field and protection against corrosion. Bis-tetrahydroacridines were newly synthesized by Pfitzinger condensation of 5,5'-(ethane-1,2-diyl) diindoline-2,3-dione with several cyclanones. NMR, MS, and FT-IR were used to prove their molecular structure. In addition, a computer-aided study was performed for the lowest energy conformers of each structure, in vacuum conditions, at ground state using DFT models to assess their electronic properties. UV-Vis and voltammetric methods (cyclic voltammetry, differential pulse voltammetry, and rotating disk electrode voltammetry) were used to investigate their optical and electrochemical properties. The results obtained for these π-conjugated heteroaromatic compounds lead to the conclusion that they have real potential in applications in different fields such as pharmaceuticals and especially as corrosion inhibitors.

Mechanism Analysis of Antimicrobial Peptide NoPv1 Related to Potato Late Blight through a Computer-Aided Study.

Phytophthora infestans (Mont.) de Bary, the oomycotic pathogen responsible for potato late blight, is the most devastating disease of potato production. The primary pesticides used to control oomycosis are phenyl amide fungicides, which cause environmental pollution and toxic residues harmful to both human and animal health. To address this, an antimicrobial peptide, NoPv1, has been screened to target Plasmopara viticola cellulose synthase 2 (PvCesA2) to inhibit the growth of Phytophthora infestans (P. infestans). In this study, we employed AlphaFold2 to predict the three-dimensional structure of PvCesA2 along with NoPv peptides. Subsequently, utilizing computational methods, we dissected the interaction mechanism between PvCesA2 and these peptides. Based on this analysis, we performed a saturation mutation of NoPv1 and successfully obtained the double mutants DP1 and DP2 with a higher affinity for PvCesA2. Meanwhile, dynamics simulations revealed that both DP1 and DP2 utilize a mechanism akin to the barrel-stave model for penetrating the cell membrane. Furthermore, the predicted results showed that the antimicrobial activity of DP1 was superior to that of NoPv1 without being toxic to human cells. These findings may offer insights for advancing the development of eco-friendly pesticides targeting various oomycete diseases, including late blight.

Nilotinib: Disrupting the MYC-MAX Heterocomplex.

MYC is a transcription factor crucial for maintaining cellular homeostasis, and its dysregulation is associated with highly aggressive cancers. Despite being considered "undruggable" due to its unstable protein structure, MYC gains stability through its interaction with its partner protein, MAX. The MYC-MAX heterodimer orchestrates the expression of numerous genes that contribute to an oncogenic phenotype. Previous efforts to develop small molecules, disrupting the MYC-MAX interaction, have shown promise in vitro but none have gained clinical approval. Our current computer-aided study utilizes an approach to explore drug repurposing as a strategy for inhibiting the c-MYC-MAX interaction. We have focused on compounds from DrugBank library, including Food and Drug Administration-approved drugs or those under investigation for other medical conditions. First, we identified a potential druggable site on flat interface of the c-MYC protein, which served as the target for virtual screening. Using both activity-based and structure-based screening, we comprehensively assessed the entire DrugBank library. Structure-based virtual screening was performed on AutoDock Vina and Glide docking tools, while activity-based screening was performed on two independent quantitative structure-activity relationship models. We focused on the top 2% of hit molecules from all screening methods. Ultimately, we selected consensus molecules from these screenings-those that exhibited both a stable interaction with c-MYC and superior inhibitory activity against c-MYC-MAX interaction. Among the evaluated molecules, we identified a protein kinase inhibitor (tyrosine kinase inhibitor [TKI]) known as nilotinib as a promising candidate targeting c-MYC-MAX dimer. Molecular dynamic simulations demonstrated a stable interaction between MYC and nilotinib. The interaction with nilotinib led to the stabilization of a region of the MYC protein that is distorted in apo-MYC and is important for MAX binding. Further analysis of differentially expressed gene revealed that nilotinib, uniquely among the tested TKIs, induced a gene expression program in which half of the genes were known to be responsive to c-MYC. Our findings provide the foundation for subsequent in vitro and in vivo investigations aimed at evaluating the efficacy of nilotinib in managing MYC oncogenic activity.

Bioinformatic Analysis and Computer-Aided Study to Investigate the Potential Application of a Bioflavonoid Compound Bilobetin in Liver Cancer Treatment.

Hepatocellular carcinoma (HCC/LIHC) is the most common type of primary liver cancer, which is a leading cause of cancer death worldwide. Patients with HCC have a short survival time after diagnosis. Unfortunately, there are no effective treatments for advanced or aggressive HCC. Thus, the rapid development of new therapeutic drugs or treatment methods for HCC is urgently needed. Bioinformatic tools and computer-aided predictions advance the processes of drug development. In this study, we incorporated bioinformatic analyses and computer-aided drug development processes to investigate the potential application of bilobetin, a bioactive compound of bioflavonoid, as a therapeutic agent for HCC treatment. Our results revealed that 4 out of 20 predicted hub target genes of bilobetin displayed functional importance in cancer-related signaling pathways in different cancers, including HCC. Importantly, the mRNA expression levels of these four key hub genes (VEGFA, SRC, MMP9, and CDK1) were significantly different between normal and HCC tumor samples. Their expression levels were significantly associated with the clinical survival outcomes of HCC patients, as well as the immune cell infiltration levels in the HCC tumor microenvironment. In addition, these four genes showed significant co-expression correlated with immune checkpoint genes, including CD274, PDCD1, CTLA4, and CD47. Furthermore, we used computer-aided approaches to investigate the binding affinity and potential binding mechanisms between bilobetin and target proteins encoded by four key hub genes. In conclusion, our study shed light on the potential application of the bioactive bioflavonoid molecule bilobetin in LIHC treatment by regulating four key hub genes.

Accuracy of computer-aided static and dynamic navigation systems in the placement of zygomatic dental implants

BackgroundZygomatic implants are widely used in the rehabilitation of severely atrophic maxillae, but implant placement is not without risks, and it can potentially cause damage to related anatomical structures. The aim of this study was to perform a comparative analysis of the accuracy of static navigation systems in placing zygomatic dental implants in comparison to dynamic navigation systems.MethodsSixty zygomatic dental implants were randomly allocated to one of three study groups, categorized by which implant placement strategy was used: A: computer-aided static navigation system (n = 20) (GI); B: computer-aided dynamic navigation system (n = 20) (NI); or C: free-hand technique (n = 20) (FHI). For the computer-aided study groups, a preoperative cone-beam computed tomography (CBCT) scan of the existing situation was performed in order to plan the approach to be used during surgery. Four zygomatic dental implants were inserted in each of fifteen polyurethane stereolithographic models (n = 15), with a postoperative CBCT scan taken after the intervention. The pre- and postoperative CBCT scans were then uploaded to a software program used in dental implantology to analyze the angular deviations, apical end point, and coronal entry point. Student’s t-test was used to analyze the results.ResultsThe results found statistically significant differences in apical end-point deviations between the FHI and NI (p = 0.0053) and FHI and GI (p = 0.0004) groups. There were also statistically significant differences between the angular deviations of the FHI and GI groups (p = 0.0043).ConclusionsThe manual free-hand technique may enable more accurate placement of zygomatic dental implants than computer-assisted surgical techniques due to the different learning curves required for each zygomatic dental implant placement techniques.

Бинарная концептуальная оппозиция «МЫ – ОНИ» в современном англоязычном политическом дискурсе

Introduction. The article is devoted to the study of the binary conceptual opposition “We – They” in modern English political discourse. One of the frequent ways of speech manipulation in political discourse is the use of the binary conceptual opposition “We – They”, which has the central role in political discourse in general. It is established that the analyzed opposition is variable and can be used at different levels – external and internal. Materials and Methods. The speeches and statements of the President of the United States of America Joe Biden for the period from January 2 to February 24, 2023 are considered as the material of the study. The frequency of use of lexical units used to represent the opposition “We – They” is determined using the LancsBox corpus manager application. Analysis. It is identified that various strategies and tactics are used in political discourse to influence the recipients of information by politicians due to the need to prove their opinion, convince the recipients of the correctness of actions, emphasize their importance and significance for the whole country. The primary objective of the study is to systematize the discursive tools of speech influence in political discourse, which are based on conceptual oppositions. Results. The significance of the results lies in the possibility of using the obtained data for an adequate perception of the information presented in the political discourse, on the one hand, and for creating own political texts, on the other hand. The scientific novelty lies in the analysis of modern material and in the effort to build a linguistic model of the use of the binary conceptual opposition “We – They” in Joe Biden’s political discourse. The novelty is also characterized by the statistical data provided, obtained using automated text processing and the creation of an electronic corpus, which can be further expanded by speeches of other political figures. The adaptability of the developed electronic corpus opens up the possibilities of the further computer-aided study of binary conceptual oppositions in political discourse with reference to the methods of digital linguistics.

RNA secondary structure packages evaluated and improved by high-throughput experiments.

Despite the popularity of computer-aided study and design of RNA molecules, little is known about the accuracy of commonly used structure modeling packages in tasks sensitive to ensemble properties of RNA. Here, we demonstrate that the EternaBench dataset, a set of more than 20,000 synthetic RNA constructs designed on the RNA design platform Eterna, provides incisive discriminative power in evaluating current packages in ensemble-oriented structure prediction tasks. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages in their standard settings, which derive parameters primarily from thermodynamic experiments. We hypothesized that training a multitask model with the varied data types in EternaBench might improve inference on ensemble-based prediction tasks. Indeed, the resulting model, named EternaFold, demonstrated improved performance that generalizes to diverse external datasets including complete messenger RNAs, viral genomes probed in human cells and synthetic designs modeling mRNA vaccines.

Proposals Generation for Weakly Supervised Object Detection in Artwork Images.

Object Detection requires many precise annotations, which are available for natural images but not for many non-natural data sets such as artworks data sets. A solution is using Weakly Supervised Object Detection (WSOD) techniques that learn accurate object localization from image-level labels. Studies have demonstrated that state-of-the-art end-to-end architectures may not be suitable for domains in which images or classes sensibly differ from those used to pre-train networks. This paper presents a novel two-stage Weakly Supervised Object Detection approach for obtaining accurate bounding boxes on non-natural data sets. The proposed method exploits existing classification knowledge to generate pseudo-ground truth bounding boxes from Class Activation Maps (CAMs). The automatically generated annotations are used to train a robust Faster R-CNN object detector. Quantitative and qualitative analysis shows that bounding boxes generated from CAMs can compensate for the lack of manually annotated ground truth (GT) and that an object detector, trained with such pseudo-GT, surpasses end-to-end WSOD state-of-the-art methods on ArtDL 2.0 (≈41.5% mAP) and IconArt (≈17% mAP), two artworks data sets. The proposed solution is a step towards the computer-aided study of non-natural images and opens the way to more advanced tasks, e.g., automatic artwork image captioning for digital archive applications.

LncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.

Accuracy of a Computer-Aided Dynamic Navigation System in the Placement of Zygomatic Dental Implants: An In Vitro Study.

The objective of this in vitro study was to evaluate and compare the accuracy of zygomatic dental implant (ZI) placement carried out using a dynamic navigation system. Materials and Methods: Forty (40) ZIs were randomly distributed into one of two study groups: (A) ZI placement via a computer-aided dynamic navigation system (n = 20) (navigation implant (NI)); and (B) ZI placement using a conventional free-hand technique (n = 20) (free-hand implant (FHI)). A cone-beam computed tomography (CBCT) scan of the existing situation was performed preoperatively to plan the surgical approach for the computer-aided study group. Four zygomatic dental implants were placed in anatomically based polyurethane models (n = 10) manufactured by stereolithography, and a postoperative CBCT scan was performed. Subsequently, the preoperative planning and postoperative CBCT scans were added to dental implant software to analyze the coronal entry point, apical end point, and angular deviations. Results were analyzed using the Student’s t-test. Results: The results showed statistically significant differences in the apical end-point deviations between FHI and NI (p = 0.0018); however, no statistically significant differences were shown in the coronal entry point (p = 0.2617) or in the angular deviations (p = 0.3132). Furthermore, ZIs placed in the posterior region showed more deviations than the anterior region at the coronal entry point, apical end point, and angular level. Conclusions: The conventional free-hand technique enabled more accurate placement of ZIs than the computer-assisted surgical technique. In addition, placement of ZIs in the anterior region was more accurate than that in the posterior region.

Numerical Modeling of Shockwave Treatment of Knee Joint.

Arthritis is a degenerative disease that primarily affects the cartilage and meniscus of the knee joint. External acoustic stimulation is used to treat this disease. This article presents a numerical model of the knee joint aimed at the computer-aided study of the regenerative effects of shockwave treatment. The presented model was verified and validated. A numerical analysis of the conditions for the regeneration of the tissues of the knee joint under shockwave action was conducted. The results allow us to conclude that to obtain the conditions required for the regeneration of cartilage tissues and meniscus (compressive stresses above the threshold value of 0.15 MPa to start the process of chondrogenesis; distortional strains above the threshold value of 0.05% characterized by the beginning of the differentiation of the tissues in large volumes; fluid pressure corresponding to the optimal level of 68 kPa to transfer tissue cells in large volumes), the energy flux density of therapeutic shockwave loading should exceed 0.3 mJ/mm2.

Computer-aided study of the impedance dispersion of biological tissues

The article considers the development of a software and hardware system to study the impedance dispersion of biological fluids and tissues using an alternating signal generated and received by SensorDaq and LabView software. The design and development of the impedance measurement software and hardware system are described in detail. The impedance and capacitance values of the biological object were obtained when scanning at various frequencies and temperatures corresponding to different object conditions. The results of the system testing are shown, and the conclusions on the interaction nature of biological tissue and an external current are drawn.

Computer-aided study and multivariate optimization of nanomolar metformin hydrochloride analysis using molecularly imprinted polymer electrochemical sensor based on silver nanoparticles

Computer-aided study and multivariate optimization of nanomolar metformin hydrochloride analysis using molecularly imprinted polymer electrochemical sensor based on silver nanoparticles

Influence of the Computer-Aided Static Navigation Technique on the Accuracy of the Orthodontic Micro-Screws Placement: An In Vitro Study.

To analyze the influence of the computer-aided static navigation technique on the accuracy of placement of orthodontic micro-screws. One hundred and thirty-eight orthodontic micro-screws were randomly assigned to the following study groups: Group A. orthodontic micro-screw placement using a computer-aided static navigation technique (n = 69); B. orthodontic micro-screw placement using the conventional freehand technique (n = 69). In addition, the accuracy in the canine–premolar, premolar and molar sectors was analyzed in each study group. Cone-beam computed tomography and intraoral scans were taken both prior and subsequent to orthodontic micro-screw placement. The images were then uploaded using a 3D implant planning software, where the deviation and horizontal angles were analyzed using a multivariate linear model. These measurements were taken at the coronal entry point and apical endpoint between the planned orthodontic micro-screws. In addition, any complications resulting from micro-screw placement, such as spot perforations, were also analyzed in all dental sectors. The statistical analysis showed significant differences between the two study groups with regard to the coronal entry-point, apical end-point (p < 0.001) and angular deviations (p < 0.001) between the computer-aided static navigation technique and freehand technique study groups. Moreover, statistically significant differences were showed between the different dental sectors (p < 0.001). Additionally, twelve root perforations were observed at the conventional free hand technique study group while there were no root perforations in the computer-aided static navigation technique study group. The results showed that the computer-aided static navigation technique enables a more accurate orthodontic micro-screw placement with less intraoperative complications when compared with the conventional freehand technique.

In Vivo Neuropharmacological Potential of Gomphandra tetrandra (Wall.) Sleumer and In-Silico Study against β-Amyloid Precursor Protein

Medicinal plants possess a surplus of novel and biologically active secondary metabolites that are responsible for counteracting diseases. Traditionally, Gomphandra tetrandra (Wall.) Sleumer is used to treat mental disorders. The present research was designed to explore phytochemicals from the ethanol leaf extract of Gomphandra tetrandra (Wall.) Sleumer to identify the potential pharmacophore(s) in the treatment of neurological disorders. The chemical compounds of the experimental plant were identified through GC-MS analysis. In-vitro antioxidant activity was assessed using different methods. Furthermore, in-vivo neurological activity was assessed in Swiss-albino mice. Computer-aided analysis was appraised to determine the best-fit phytoconstituent of a total of fifteen identified compounds in the experimental plant extract against beta-amyloid precursor protein. The experimental extract revealed fifteen compounds in GC-MS analysis and the highest content was 9, 12, 15-octadecatrienoic acid (z,z,z). The extract showed potent antioxidant activity in in-vitro assays. Furthermore, in in-vivo neurological assays, the extract disclosed significant (p &lt; 0.05) neurological activity. The most favorable phytochemicals as neurological agents were selected via ADMET profiling, and molecular docking was studied with beta-amyloid precursor protein. In the computer-aided study, 1, 5-diphenyl-2h-1, 2, 4-triazoline-3-thione (Pub Chem CID: 2802516) was more active than other identified compounds with strong binding affinity to beta-amyloid precursor protein. The present in vivo and in silico studies revealed neuropharmacological features of G. tetrandra leaf extract as a natural agent against neurological disorders, especially Alzheimer’s disease.

Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis.

Iconography studies the visual content of artworks by considering the themes portrayed in them and their representation. Computer Vision has been used to identify iconographic subjects in paintings and Convolutional Neural Networks enabled the effective classification of characters in Christian art paintings. However, it still has to be demonstrated if the classification results obtained by CNNs rely on the same iconographic properties that human experts exploit when studying iconography and if the architecture of a classifier trained on whole artwork images can be exploited to support the much harder task of object detection. A suitable approach for exposing the process of classification by neural models relies on Class Activation Maps, which emphasize the areas of an image contributing the most to the classification. This work compares state-of-the-art algorithms (CAM, Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++) in terms of their capacity of identifying the iconographic attributes that determine the classification of characters in Christian art paintings. Quantitative and qualitative analyses show that Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++ have similar performances while CAM has lower efficacy. Smooth Grad-CAM++ isolates multiple disconnected image regions that identify small iconographic symbols well. Grad-CAM produces wider and more contiguous areas that cover large iconographic symbols better. The salient image areas computed by the CAM algorithms have been used to estimate object-level bounding boxes and a quantitative analysis shows that the boxes estimated with Grad-CAM reach 55% average IoU, 61% GT-known localization and 31% mAP. The obtained results are a step towards the computer-aided study of the variations of iconographic elements positioning and mutual relations in artworks and open the way to the automatic creation of bounding boxes for training detectors of iconographic symbols in Christian art images.

Design and simulation study of double side-cavity gridless reltron with dual extraction sections

Reltron is a high power microwave (HPM) oscillator with many attractive features. Here, we report a computer-aided design and simulation-based study of a double side-cavity (DSC) gridless reltron, which is an advancement of the conventional reltron. Commercially available software package CST Microwave Studio (MWS) has been used for the device simulation. The modulation section has been designed and studied by obtaining the S-parameters. From the electromagnetic (EM) field patterns and the frequencies of normal modes, we identify an extra mode in a DSC reltron, in addition to the three normal modes of a conventional reltron – (0, , and π). Furthermore, the whole device has been modeled and simulated using particle in cell (PIC) solver to study the device performance under different post acceleration (PA) potentials. The use of the dual extraction section results in a peak efficiency of 38.6%.

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study.

At the end of December 2019, a novel strain of coronavirus, given the name of 2019-nCoV, emerged for exhibiting symptoms of severe acute respiratory syndrome. The virus is spreading rapidly in China and around the globe, affecting thousands of people leading to a pandemic. To control the mortality rate associated with the 2019-nCoV, prompt steps are needed. Until now there is no effective treatment or drug present to control its life-threatening effects in the humans. The scientist is struggling to find new inhibitors of this deadly virus. In this study, to identify the effective inhibitor candidates against the main protease (Mpro) of 2019-nCoV, computational approaches were adopted. Phytochemicals having immense medicinal properties as ligands were docked against the Mpro of 2019-nCoV to study their binding properties. ADMET and DFT analyses were also further carried out to analyze the potential of these phytochemicals as an effective inhibitor against Mpro of 2019-nCoV.

Binding Dynamics of siRNA with Selected Lipopeptides: A Computer-Aided Study of the Effect of Lipopeptides' Functional Groups and Stereoisomerism.

The engineering issues pertaining to nanoparticle systems toward targeted gene therapies have not been fully probed. Recent experiments have identified specific structural characteristics of a novel class of lipopeptides (LP) that may lead to potent nanocarriers intended as RNAi therapeutics, albeit the molecular mechanism that underlies their performance remains unexplored. We conducted molecular dynamics simulations in atomistic detail coupled with free energy computations to study the dynamics and thermodynamics of an acrylate- and an epoxide-derived LP, members of the aforesaid class, upon their binding to siRNA in aqueous solution aiming at examining structure-potency relations. We found that the entropic part of the free energy of binding predominates; moreover, the first LP class tends to disrupt the Watson-Crick base pairing of siRNA, whereas the latter leaves the double helix intact. Moreover, the identified tug-of-war effect between LP-water and LP-siRNA hydrogen bonding in the supramolecular complex can underpin synthesis routes toward tuning the association dynamics. Our simulations on two diastereomers of the epoxide-derived LP showed significant structural and energetics differences upon binding, as a result of steric effects imposed by the different absolute configurations at their chiral centers. These findings may serve as crucial design parameters toward modulating the interplay between complex stability and ease of releasing the nucleic acid drug into the cell.

Computer-aided study of selective flavonoids against chikungunya virus replication using molecular docking and DFT-based approach

Chikungunya fever has a high morbidity rate in humans and is caused by chikungunya virus (CHIKV). Currently, there is no vaccination or treatment available to show an effective efficacy against this disease. This study targets four non-structural proteins of CHIKV using 650 flavonoids from various medicinal plants, inhabited in Pakistan and India. The compounds are initially screened on the basis of their effective pharmacological properties and are docked against the four proteins. A threshold of − 8.5 kcal/mol is applied to screen and reduce the number of flavonoids for further analysis. The reactivity of screened flavonoids is analyzed using the density functional theory (DFT). Cirsimaritin, apigenin, tamarixetin, and 5,7,3′,4′-tetrahydroxyflavone from Andrographis paniculata have shown a high binding affinity against nsP1. Rhamnetin, tamarixetin and medioresinol have shown a strong binding affinity against nsP2. Four flavonoids, i.e. 5,7,3′,4′-tetrahydroxyflavone, 5,7,4′-trihydroxyflavone, tamarixetin and rhamnetin, showed a high binding affinity for nsP3 while apigenin depicted a strong binding affinity for nsP4. Pharmacological properties of these flavonoids illustrate an effective disposition in humans. The results manifest that the screened eight flavonoids can be analyzed against CHIKV for in vitro and in vivo cell replication, due to their effective pharmacological properties, strong inhibition and high reactivity.