Virtual Screening and Network Pharmacology-Based Study to Explore the Pharmacological Mechanism of Clerodendrum Species for Anticancer Treatment

Background Cancer is a second leading cause of death in the world, killing approximately 3500 per million people each year. Therefore, the drugs with multitarget pharmacology based on biological networks are crucial to investigate the molecular mechanisms of cancer drugs and repurpose the existing drugs to reduce adverse effects. Clerodendrum is a diversified genus with a wide range of economic and pharmacological properties. Limited studies were conducted on the genus's putative anticancer properties and the mechanisms of action based on biological networks remains unknown. This study was aimed to construct the possible compound/target/pathway biological networks for anticancer effect of Clerodendrum sp. using docking weighted network pharmacological approach and to investigate its potential mechanism of action. Methods A total of 194 natural Clerodendrum sp. Compounds were retrieved from public databases and screened using eight molecular descriptors. The cancer-associated gene targets were retrieved from databases and the function of the target genes with related pathways were examined. Cytoscape v3.7.2 was used to build three major networks: compound-target network, target-target pathway network, and compound-target-pathway network. Results Our finding indicates that the anticancer activity of Clerodendrum sp. involves 6 compounds, 9 targets, and 63 signaling pathways, resulting in multicompounds, multitargets, and multipathways networks. Additionally, molecular dynamics (MD) simulations were used to estimate the binding affinity of the best hit protein-ligand complexes. Conclusion. This study suggests the potential anticancer activity of Clerodendrum sp. which could further contribute to scavenger novel compounds for the development of new alternative anticancer drugs.


Introduction
Cancer is a neoplasmic disease that regulates uncontrolled cell division and leads to abnormal growth of cell mass. In 2018, the World Cancer Report estimated about 1.16 million new cases with 784,800 deaths and 2.2.6 million in 5-year prevalent cases in India. Te National Cancer Institute had listed eight categories for the cancer treatment which include chemotherapy, radiation, surgery, targeted therapy, stem cell transplant, and medicines and immunotherapy [1]. Some of these therapies have resulted in drug resistance after initial positive response which is termed as acquired drug resistance that occur in both cytotoxic chemotherapies and targeted therapies. Moreover, cancer involves the interactions of multiple genes as well as functional proteins. Te success of drug discovery in cancer remains unsolved due to diversity of cancer types, excessive toxicity, constraints efcacy, and acquired treatment resistance [2]. Tis complication in cancer treatment arises due to the failure of "one gene, one drug, one disease" paradigm [3,4]. Terefore, the developments of new potent and nontoxic treatments are highly active in scientifc research [5].
With the development of computational approach, disease networks constructed through network biology are suggested as powerful tools for screening out drugs targets. Network pharmacology developed by Hopkins enhances high rate of clinical success with less side efects, and around 40% of the current drugs discoveries were contributed by this approach [3]. Tis approach is best appreciated in anticancer research, where both genetic and nongenetic bypass mechanism has led to inherent in diferent cancer phenotypes [6,7].
Te practices of natural products from plants are widely accepted as potential new lead source towards the discovery of new alternative anticancer drug. Te secondary metabolites extracted from plants were considered as the primary source of drugs in Indian and other ancient systems of medicine in the world due to its higher structural diversity, metabolism in the body with low toxicity, and complexity than synthetic drugs [8]. Tese bioactive natural compounds could inhibit potential targets and reduce the cost of new drug development due to its availability in nature as well as provide option for combination therapies [9]. Since 1981 to 2014, a total of 174 compounds were approved for commercialization in the treatment of cancer. Many studies suggested that the modulation of multitargets rather than single targets could lead to discovering efective drugs [10]. Several studies reported the use of diferent plant-based resources for the treatment of cancer with scientifc validations but a majority of plants were still left to be documented [11,12]. Te Clerodendrum sp. reported to inhibit cancer activity but its efciency and mechanism remain unclear until today [13]. In this study, we focused on chemical constituents of Clerodendrum to be efective against cancer. Te genus Clerodendrum consists of 580 species that are widely distributed in tropical and subtropical regions of the world that comprises of small trees, shrubs, and herbs [14]. Many researchers isolated and identifed diferent biological active compounds and other major chemical constituents such as favonoids, phenolics, steroids, and terpenoids from the genus [15][16][17]. More than 280 major chemical constituents from various species of Clerodendrum were isolated and identifed till date. Many species of this genus were used as folk medicines by various tribes of African and Asian continents for the treatment of lifethreatening diseases, anticancer, antitumor, antidiabetic, antihypertensive, and antidiarrhoel activities [18,19]. Besides its medicinal importance, this genus has ornamental values. Species such as C. thomsoniae, C. indicum, C. panniculatum, C. inerme, C. japonicum, and C. speciosum are cultivated for their aesthetic values. C. inerme, C. thomsonia, and C. splendens are among the most sought for cultivation in gardens, for covering fences and walls.
Even though there are many studies on pharmacological efects of the genus but the anticancer activity of the genus based on in silico analysis and network pharmacology were elucidated till date. Terefore, this study was aimed to explore the active compounds, potential targets, and biological pathways of the Clerodendrum sp. using molecular docking, network pharmacology, and molecular simulations analysis, which could further provide basis for subsequent studies and clinical applications. Te detail fowchart of this study was depicted in Figure 1.

Collection of Natural Compounds and Space Analysis.
Te bioactive natural compounds (NCs) of Clerodendrum sp. were retrieved through extensive literature survey [20][21][22][23]. Te two dimensional (2D) structures of the compounds were retrieved from PubChem and Chemspider databases and prepared into a library. Te duplicate structures were deleted and some compounds which did not have specifc structures were sketched using the Marvin Sketch v6.2.0 and saved in.mol2 format. Te two dimensional (2D) structures were converted into 3D structures by using Corina 3D analysis tool in Tsar (Tools for structure activity relationships) software (https://www. accelrys.com/). Further, the three dimensional (3D) structures were converted to.pdb format. Te dataset of compounds was further analysed, and hydrogen was added using CHARMm based on smart minimizer that generates 1500 steps of Steepest Descent followed by conjugate gradient algorithms with a convergence gradient of 0.001 kcal mol. Te details of bioactive compounds retrieved from Clerodendrum sp. are listed in Table S1.
Additionally, 43 approved drugs molecules related to cancer were collected from Drug Bank and selected as reference to analyse the NCs library. Te molecular descriptors of NCs and drugs were calculated using the PaDEL-Descriptor software.
Te principal component analysis (PCA) was conducted on the NCs and drug library by using the BioVinci tools to visualize the distribution of libraries in the chemical space. Te PCA analysis was performed with 8 molecular descriptors: ALogP, Molecular Weight, Number H-Donors, Number-H Acceptors, Number Rotatable Bonds, Number of Rings, Number of Aromatic Rings, and Molecular Fractional Polar Surface Area.

ADME and Toxicity
Profling. Te ADMET study refers to the pharmacokinetics approach of a molecule where absorption, distribution, metabolism, excretion, and toxicity of the compounds could be analysed. Te ADME and toxicity properties of the selected NCs were predicted using PreADMET server and Osiris Property Explorer (https:// www.openmolecules.org/propertyexplorer/applet.html).

Retrieval of Cancer Targets.
Te protein targets associated with human cancer were collected from four resources: (i) Terapeutic Target Database (TTD) [24], (ii) DrugBank [25], (iii) Uniport [26], and (iv) Protein Data Bank [27]. Te duplicates structures, structures with no active site, incomplete structures, and structures from other organisms were deleted. Finally, 60 important drug targets from key type 'Homo sapiens' were selected. Te information of the collected protein targets is listed in Table S2. Te downloaded PDB structures were selected based on the parameters such as (a) Protein extracted from X-ray difraction process and (b) must contain one or more active sites for binding of ligands. All the structures were cleaned and optimized using USCF Chimera [28]. Among 60 3D protein structures, the resolutions of crystal structures were found to be ranging from 1.2Å to 3.5Å.

Protein-Protein Interaction (PPI) Network Analysis.
Te PPI data was obtained from STRING 11.0 (Search Tool for the Retrieval of Interacting Genes/Proteins) with species restricted to "Homo sapiens." Tis database constructs nodes and edges of network to represent proteins and proteinprotein associations. To ensure high confdence information, the minimum score was set to >0.9 and excluded the disconnected proteins in the networks. Te potential target proteins and protein pathways involved in cancer were recorded in an excel table and imported into the Cytosca-pev3.7.2 to obtain target pathway network.

Gene Ontology (GO) and Koyo Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis of Cancer Targets.
All the potential target genes were uploaded to DAVID (Database for Annotation, Visualization and Integrated Discovery) database. Te identifer was selected as 'OFFICIAL GENE SYMBOL', and species were selected as 'Homo sapiens' for GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway annotation [29].
2.6. Molecular Docking Approach. Te bioactive NCs were docked to target protein using PyRx tool [30]. In this study, AutoDockVina, which allows fexible docking in active site by allowing fexibility in the ligand, was used for docking [31]. Blind docking was performed in NCs against cancer targeted proteins in order to detect the possible binding sites and modes of the ligands by examining the complete mass of targeted protein. Based on docking score the results were analysed and ranked accordingly. Further, these docking results were subjected to the construction of biological networks to study the polypharmacological nature of selected NCs as cancer inhibitors.

Network Pharmacology Study.
Network Pharmacology approach was used to predict the polypharmacological potency of NCs to identify potential anticancer drugs [32]. Te three networks were constructed as follows: (i) compound-target network, (ii) target-target pathway network, and (iii) compound-target-pathway network for better understanding of the active compounds and their mechanism against cancer. All the networks were visualized with the Cytoscape v3.7.2 software and analysed using the Network Analyzer plugin [33].  Evidence-Based Complementary and Alternative Medicine targets. In this study, NVIDIA RTX 1060 GPU accelerated GROMACS 2021 software; running over Linux Ubuntu 20.04 LTS operating system was used. Te topology of protein and ligands were generated using Charmm36 force feld and SwissPARAM server [34]. Te solvation of each system was performed with TIP3P water model followed by neutralization with suitable Na + and Cl − ions. Te energy was minimized by the steepest descent minimization algorithm with maximum 50,000 steps. Position restrains were applied to receptor and ligand of the each systems for 100 ps throughout heating (300 K) utilizing NVT (No. of atoms, Volume, Temperature) together with leap-frog integrator, 2 fs time step, and LINCS holonomic constraints. After NVT, NPT (No. of atoms, Pressure, Temperature) equilibration was conducted at 300K temperature for 100 ps using 2 fs time step. Finally, 10 ns MD production run was generated without any restrain with 2 fs time step, and structures were recorded at each 10 ps coordinates. Te trajectories fles of root mean square deviation (RMSD), root mean square fuctuation (RMSF), Radius of gyration (Rg), and a number of hydrogen bond analysis (H-bonds) were calculated after completion of MD simulations for further analysis.

Screening of Active NCs of Clerodendrum sp.
A total of 194 NCs of Clerodendrum sp. were screened to create a compound library and evaluated with the eight molecular descriptors such as molecular weight (MW), number of hydrogen bond acceptors (HBAs), number of hydrogen bond donors (HBDs), total polar surface area (TPSA), and the drug-like properties of the NCs and drugs. In this study, it was found that out of 194, 126 natural products satisfed the eight physiological conditions and the details are provided in Table 1. A similar set of results were analysed for reported drug compounds. Further, a target library of 60 cancer proteins was compiled. Te NCs of Clerodendrum and reported drug libraries were further evaluated using PCA to visualize its allocation in the chemical space. Te eight molecular descriptors were used to generate the PCA model. We found that the distributions of NCs (Red sphere) are analogous to the 3D space occupied by the reported compounds (Blue sphere) and specify the resemblance of drug like assets in NCs with potential anticancer activity. Te variances of PCA1, PCA2, and PCA3 are -1.98, -1.45, and -3.27, respectively ( Figure 2). Te structures of 126 compounds were imported to PreADMET server and Osiris Property Explorer. Te results of ADME/toxicity screening showed that 58 compounds had good ADME parameters and relatively safe to be considered as drug-like compounds for inhibition of cancer. Hence, the selected NCs satisfes the overall parameters to be taken over as positive drugs and listed in Table 2 where "Green" indicates drug-conform behaviour.

GO and KEGG Pathway Enrichment Analysis.
Te potential 60 cancer targets genes were uploaded to DAVID 6.8 database for GO annotation and KEGG pathway analysis.
Te threshold was set as P ≤ 0.05, and the pathways or gene function with maximum count were analysed further.

Analysis of Target Proteins in Protein-Protein Interaction
Network. PPI network was constructed to analyse the physical interactions of 60 cancer proteins by using STRING software. Te data were downloaded in CSV fle format and imported into Cytoscape v3.7.2 software [10]. In PPI networks, a higher degree value of a node indicates the importance of node in the network. A total of 149 edges and 52 nodes with an average node degree of 4.97 were obtained the in PPI network after fltering the confdence level >0.9 and rejecting the target protein independent of the network. Te network analyser tool indicates that the proteins MAPK1, TP53, HSP90AA1, SRC, PIK3CA, HRAS, EGFR, ESR1, ERBB2, PRKCA, AURKA, BRCA1, AR, PGR, JAK2, PDPK1, PPP2CA, TYMS, TOP2A, AURKB, CDK2, and TERT contained degree higher than average node degree, which means that these proteins might play an essential role of bridge to connect other nodes in PPI network ( Figure 5).

Molecular Docking of NCs with Cancer Targets.
In this study, the 58 screened NCs were docked with 60 selected cancer target proteins using AutoDockVina in the PyRx tool. Te NCs were considered as ligands against 60 targeted cancer genes. A similar method was applied for the reported drugs to have a comparative study. All the NCs were found to bind with 60 potential targets resulting in diferent docking scores. Based on the docking score a threshold value of lower than −8.0 kcal/mol was selected and ranked the compounds based on this threshold value ( Figure 6). Te cancer target PDB ID 1RY0 (AKR1C3) showed the highest interactions with the majority of the NCs, followed by 1HFQ (DHFR), 3ZGC (NFE2L2), 4AF3 (AURKB), 4K33 (FGFR3), 5P21 (HRAS), 2I0V (CSF1R), and 3ZIM (PIK3CA) protein targets and least interaction was found with the 1M9Z (TGFBR2) protein. Further, these docking results were subjected for the NCs-Cancer target network construction in order to study the polypharmacological nature of selected NCs as cancer inhibitors.

Compound-Target (C-T) Network
Analysis. Te C-T network was constructed with a docking score (lower than −8.0 kcal/mol) to understand the mechanism of action between NCs of Clerodendrum and cancer targets (Figure 7). Te C-T network consists of 118 nodes (58 compounds and 60 potential targets) and 1492 edges (C-T interactions). Te possible interaction between NCs and target proteins wasevaluated with important topological parameters such as degree, average betweenness centrality of nodes, closeness centrality, average shortest path length, and Topological Coefcient. We found that 57 compounds (expect L13) interact with multiple targets and 59 targets expect TGFBR2 interacts multiple compounds in the constructed network. Tus, these fndings indicate multicomponents multitargets interaction in Clerodendrum sp.

Target-Target Pathway Analysis (T-P).
A total of 63 pathways related to cancer (P ≤ 0.05) were obtained by placing cancer-targeted genes in DAVID for KEGG pathway analysis. We constructed the T-P network by combining the pathways with cancer-related gene information. Tis network analyses the interaction of cancer targets and relationship between pathways and targets ( Figure 8). Te network consists of 106 nodes (63 pathways and 43 genes) and 435 edges. Te results showed that pathway in cancer contained maximum genes (22°) followed by PI3K-Akt signaling pathway, proteoglycan in cancer, prostate cancer, and focal adhesion could play a crucial role in the treatment of cancer and regulates the complex biological and metabolic processes. Tese pathways could be suggested as potential signaling pathways that mediate the efects of Clerodendrum NPs against cancer. Moreover, the target genes such as MAPK1, PIK3CA, HRAS, EGFR, TP53, PRKCA, PDPK,1and SRC genes could act on more than 18 pathways. Interfering target genes of these pathways could be a potential strategy for the future treatment of cancer disease.

Compound-Target-Pathway Network Analysis (C-T-P).
Te C-T-P network was constructed based on active compounds, target identifcation, and pathway analysis. Tis     Evidence-Based Complementary and Alternative Medicine  intervene the potential pathway such as pathway in cancer, PI3K-Akt signaling pathway, proteoglycans in cancer, MAPK signalling pathway, focal adhesion, and prostate cancer for treating cancer in future.

MD Simulation of Scutellarein and 17-HydroxyteuvincenoneG with 1ry0 Cancer Target.
To study the stability of protein-ligand complexes, MD simulations were performed for 10 ns with the top 2 NCs (Scutellarein and 17-hydroxyteuvincenone G) and 1ry0 cancer target along with PG2 the co-crystallized ligand as the standard. Te MD trajectories of Scutellarein-1ry0 and 17-hydroxyteuvincenoneG-1ry0 complexes were compared with co-crystallize ligand prostaglandin D2 (PG2)-1ry0 and values of RMSD, RMSF, Rg, and H-bond were recorded. Te complex with a lesser RMSD value could be more stable as compared to higher RMSD values. Te average RMSD values for Scutellarein-1ry0 and 17-hydroxyteuvincenoneG-1ry0 complexes were found to be 0.29 nm and 0.34 nm, respectively, which are signifcantly stable as compared to the native co-crystal ligand PG2 (0.56 nm). Te Scutellarein-1ry0and17-hydroxyteuvincenone G-1ry0 complexes had a lower RMSD value that range from  Evidence-Based Complementary and Alternative Medicine 0.06 nm to 0.59 nm, 0.07 nm to 0.76 nm, while PG2-1ry0 complexes range from 0.06 nm to 0.98 nm (Figure 10(a)). It indicates that Scutellarein and17-hydroxyteuvincenoneG formed stable complexes with 1ry0 as compared to cocrystallize ligand PG2. Te backbone RMSF of each residue was calculated to access the amino acid residues contributing to the complex structural fuctuations. Te average RMSF values of Scutellarein-1ry0, 17-hydroxyteuvincenoneG-1ry0 and PG2-1ry0were 0.14 nm, 0.23 nm, and 0.57 nm, respectively. From the graph Figure 10(b), we could notice that there was an overall decrease in the fuctuation of Scutellarein-1ry0 as compared to 17-hydroxyteuvincenoneG and PG2. Tis suggests that Scutellarein was more stable and rigid than the 17-hydroxyteuvincenoneG structure and PG2.  Te analysis of Rg is considered as root mean square distances from each atom of the system to its centre of mass. As depicted in Figure 10(c), the Rg of Scutellarein had a lower value as compared to 17-hydroxyteuvincenoneG and PG2, which indicates that Scutellarein had a compact and stable complex with 1ry0 during the MD simulation period. Additionally, the H-bonds indicate a crucial role in molecular recognition and determine the stability of the drugprotein complex structure. In Figure 10(d), the intermolecular H-bond trajectories with time-dependent bond distances variation for 10 ns were illustrated. Te average number of hydrogen bonds in the standard ligand PG2 (0.13 nm) was less than 17-hydroxyteuvincenoneG-1ry0 (0.58 nm) and Scutellarein-1ry0 (2.12 nm) throughout the MD simulation time period. Te lesser number of H-bond in the PG21-1ry0 complex makes it relatively unstable and the higher number of H-bonds in Scutellarein-1ry0 and 17-hydroxyteuvincenoneG-1ry0 represent the stability of the complexes. Overall, the analysis indicates that Scutellarein as compared to 17-hydroxyteuvincenoneG and PG2 forms a stable protein-ligand complex with 1ry0 cancer target protein and does not depict any conformational change in protein structure during the simulation process.

Discussion
Some of the developed cancer therapies have resulted in drug resistance against the synthetic drugs [35]. Terefore, natural products are considered as a potential new source of targets for drug discovery as it had higher structural diversity and could reduce the cost of new drug development due to their availability in nature. Terefore, in this study 194 NCs of Clerodendrum sp. were evaluated against 60 reported human cancer proteins. All the compounds were initially screened based on molecular descriptors and PCA analysis was evaluated to determine the relationship between statistically meaningful conformations sampled during the trajectory. ADMET profling of compounds was considered as signifcant features for drug development [36]. Te parameters such as Blood Brain Barrier Penetration (BBB), Caco2 cell permeability, Human Intestinal Absorption (HIA), Madin-Darby canine kidney cells (MDCK), Plasma Protein Binding (PPB), and Skin Permeability (SP) were selected to screen ADME properties of compounds. For toxicity prediction, parameters such as Mutagenic, Tumorigenic, Irritant, Reproductive efective, Druglikness, and Drug score were used. A total of 58 NCs were found to be    Evidence-Based Complementary and Alternative Medicine ADME/toxicity positive drug-like compounds for further study. Te possible mechanism of action and signaling pathways associated with the 60 cancer genes were elucidated with enrichment analysis. Te GO enrichment analysis of cancer proteins recorded 29 charts, 45 charts, and 161 charts for CC, MF, and BP analysis. KEGG pathway analysis was performed to examine the signaling pathways and functions of the identifed target genes [37]. Te analysis of KEGG pathways found that 63 pathways were signifcantly correlated with target genes. Te highest enrichment scores in cancer pathways were obtained in the PI3K-Akt signaling pathway, proteoglycan in cancer, and prostate cancer. Te pathway in cancer depicts similar signifcance in cancer apoptosis, metastasis, cell proliferation, survival, and angiogenesis [38]. Te activation of PI3K triggers the phosphorylation of PIP2 to PIP3 that phosphorylates AKT to regulate the metabolism of insulin [39]. Te PI3K-Akt signaling pathway involves tumor cell apoptosis and autophagy [40]. Tese results supported the application of Clerodendrum compounds in treating cancer. PPI networks were constructed with 52 nodes and 149 edges and  Te docking weighted network pharmacology approach was employed to explore the polypharmacological efects of Clerodendrum sp. compounds in the treatment of cancer by analysing network interaction between C-T, T-P, and C-T-P. Te network between the C-T was evaluated using degree and betweenness centrality, which indicates the number of related nodes in the network. Te C-T network suggested that 6 compounds L51 (Scutellarein), L2 (17-hydroxyteuvincenone G), L54 (Teuvincenone A), L10 (Acacetin), L55 (Teuvincenone E), and L50 (Scutellarein 7-glucuronide) had potential pharmacological activities against cancer ( Figure 11). Tis study showed similar comparisons with various works of literature in citing these compounds as anticancer agents as such the compound Scutellarein and Scutellarein 7-glucuronide, favones glycoside was reported to exhibit antiproliferative and antiapoptotic activities among various human malignancies [41,42]. Similarly, 17-hydroxyteuvincenoneG showed signifcant cytotoxicities activities against the growth of human promyelocyticleukemia (HL-60) and human lung adenocarcinoma epithelial (A-549) tumor cell lines [43]. Teuvincenone A and E had remarkable in-vitro cytotoxicity activity against human cell lines [44], and acacetin exerts an antiproliferative efect by blocking cell cycle progression and could enhance the therapeutic potential in nonsmall cell lung cancer cell [45] and breast cancer [46]. Te nine target proteins with high degree including AKR1C3 (Aldo-ketoreductase family 1 member C3), DHFR (Dihydrofolate Reductase), NFE2L2 (Nuclear factor erythroid 2-related factor 2-Like 2), AURKB (Aurora B Kinase), CD44 (CD44 antigen), PIK3CA (Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform), FGFR3 (Fibroblast growth factor receptor 3), CSF1R (Macrophage colony-stimulating factor 1 receptor), and HRAS (GTPaseHras) were identifed as a key target of Clerodendrum sp. in the treatment of cancer ( Figure 12). Terefore, targeting these mutant proteins with novel therapeutics agents could eliminate the morbidity and mortality of human cancer.
Te network analysis of T-P and C-T-P supports different pathways such as Pathway in cancer, PI3K-Akt signaling pathway, Proteoglycans in cancer, MAPK signaling pathway, Focal adhesion, and Prostate cancer as potential signaling pathways to mediate the signifcant efects of Clerodendrum compounds against cancer.
MD simulation could determine the underlying dynamics of protein-ligand interactions [47]. Based on Network pharmacology, the MD simulation was performed for 10 ns with PG2 co-crystallize ligand as standard and determined the stability of NCs with the cancer target protein.
Te top 2 bioactive compounds (Scutellarein and 17hydroxyteuvincenone G) had maximum interactions with cancer targets and the 1ry0 cancer target based on the highest docking scores was used as starting point for MD simulation analysis.Te analysis of MD trajectories revealed that Scutellarein and17-hydroxyteuvincenoneG had favourable conformational stability, fexibility, and binding energy when docked with 1ry0 as compared to the cocrystallized structure of the standard 1ry0-PG2 complex. Terefore, Scutellarein and17-hydroxyteuvincenoneG could be proposed as efective compounds in inhibition of protein target for further in-vitro and in-vivo anticancer studies. Many studies suggest that modulation of multi-targets rather than single targets could lead to discovering efective drugs [48]. Hence, these fndings could beneft from the current knowledge of drug-protein interaction and relate pharmacological space with genomic space in order to treat cancer disease.

Conclusions
A total of 58 compounds were screened based on molecular descriptors, PCA analysis, and ADME/toxicity. Te 60 cancer target genes were analysed with GO annotation, KEGG pathway, and PPI interactions. Te selected NCs and cancer targets were analysed with network-related tools to confrmed and reveal the potential anti-cancer activity and molecular mechanism of Clerodendrum compounds. Te result of Network pharmacology analysis revealed that 6 active compounds exert their anti-cancer activity with 9 targets in 63 pathways. Tese results suggested that the identifed compounds might contribute to regulate with diferent cancer related genes and signaling pathways with potential applications in cancer treatment. Further in-vivo experimental studies are still in demands to validate our fndings as this study was performed based on data analysis and contributes to scavenger new source of compounds and the development of new anti-cancer drugs.

Data Availability
Te data are available within the manuscript and also accessible from the corresponding author upon request.

Conflicts of Interest
Te authors declare no conficts of interest.