Drug Discovery Research Articles

Development Validation of UV Spectrophoto Meter Method for Determination of Polyherbal Powder

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Alkylazolation of Alkenes via Photocatalytic Radical Polar Crossover.

We present a photocatalytic method for alkylamination of alkenes, enabling efficient C-C and C-N bond formation to construct aza-heterocycles valuable in drug discovery. Using a radical-polar crossover pathway, electron-deficient alkenes are reduced to electrophilic radicals, which react with electron-rich alkenes to form nucleophilic radicals. Oxidation of these intermediates yields carbocations, which are trapped by aza-heteroarenes to afford alkylaminated products. This strategy enhances molecular complexity while suppressing side reactions.

Relationship Between Pituitary Gland and Stem Cell in the Aspect of Hormone Production and Disease Prevention: A Narrative Review.

In the last two decades, scientists have gained a better understanding of several aspects of pituitary development. The signaling pathways that govern pituitary morphology and development have been identified, and the compensatory relationships among them are now known. This paper aims to emphasize the wide variety of relationships between Pituitary Gland and Stem cells in hormone Production and disease prevention. Based on many case reports and several types of research, a wide variety of relationships between the Pituitary Gland and Stem cells in the aspect of hormone Production and disease prevention are reviewed in this literature. In this paper, we focus on various roles and functions of the pituitary gland, the responsibilities of stem cells as a mode of hormone production, and disease prevention. Within this period, more was discovered concerning the contributions made to the transcription factors within the pituitary development, with factors such as Prop1, Pitx1, and Sox2 being defined as important in the development and action of hormone-secreting cells. They are also required in the appropriate specification of the cell types in the pituitary gland and the persistence of the progenitors. Manipulation of these factors causes developmental defects as well as tumors, thus the necessity of knowing the precise function and interaction of these factors. A closer look at these transcription factors could help expand treatment options for structural defect development or give rise to pituitary adenomas. It has been established that signaling pathways such as Sonic Hedgehog (Shh), Wnt, and Notch play a part in modulating pituitary development. These pathways are involved in regulating important processes such as cellular proliferation, differentiation, and organization of the pituitary gland tissues. Breaching any of these pathways has been correlated with the development of various pituitary-related conditions including adenomas and congenital hypopituitarism. Moving forward, further studies of these pathways and their associations with stem cells could provide a better understanding of disease processes and approaches to manage them. This way, there is a possibility of developing new approaches aimed at treating the cause of the dysfunction of the pituitary gland by modulating its specific signaling activities. Promising directions for the stimulation of hormone synthesis and restoration of normal pituitary function upon its disorders via tissue regrowth could be found in stem cell application. The fact that one can generate functional pituitary cells from iPSCs for instance provides new avenues both for the understanding of pituitary disease mechanisms as well as for personalized medicine. It is possible to utilize these stem cell- derived cells for modeling disease, drug discovery or even transplantation to restore the function of the damaged pituitary gland. In the future, however, the focus ought to be on the effective application of stem cell therapies that have been research during the development of better differentiation processes. The recent understanding of the system that carries the hypothalamic hormones to the pituitary gland, i.e., the hypophyseal portal vasculature, has had its implications too. This factorial consideration emphasizes the role of the vascular component in the control of pituitary activity - the release of hormones by the pituitary gland. Exploring stem cell-hypophyseal portal system interactions may open new avenues of treatment for diseases associated with deficient hormone transportation and/or pituitary dysfunction.

Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities

The healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, limited accessibility, shortage of skilled professionals, and high-quality care. Artificial intelligence (AI)-driven solutions have the potential to lessen the stress on India’s healthcare system; however, integrating trustworthy AI in the sector remains challenging due to ethical and regulatory constraints. This study aims to critically review the current status of the development of AI systems in Indian healthcare and how well it satisfies the ethical and legal aspects of AI, as well as to identify the challenges and opportunities in adoption of trustworthy AI in the Indian healthcare sector. This study reviewed 15 articles selected from a total of 1136 articles gathered from two electronic databases, PubMed and Google Scholar, as well as project websites. This study makes use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. This study identifies a gap in the adoption of trustworthy AI in Indian healthcare and various challenges associated with it. It explores opportunities for developing trustworthy AI in Indian healthcare settings, prioritizing patient safety, data privacy, and compliance with ethical and legal standards.

MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.

Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi-modal deep learning framework, is developed to predict functional phosphorylation sites. MMFuncPhos outperforms existing functional phosphorylation site prediction approaches. EFuncType is further developed based on transfer learning to predict whether phosphorylation of a residue upregulates or downregulates enzyme activity for the first time. The functional phosphorylation sites predicted by MMFuncPhos and the regulatory types predicted by EFuncType align with experimental findings from several newly reported protein phosphorylation studies. The study contributes to the understanding of the functional regulatory mechanism of phosphorylation and provides valuable tools for precision medicine, enzyme engineering, and drug discovery. For user convenience, these two prediction models are integrated into a web server which can be accessed at http://pkumdl.cn:8000/mmfuncphos.

Probing Biomolecular Interactions with Paramagnetic Nuclear Magnetic Resonance Spectroscopy.

Recent advances in computational methods like AlphaFold have transformed structural biology, enabling accurate modeling of protein complexes and driving applications in drug discovery and protein engineering. However, predicting the structure of systems involving weak, transient, or dynamic interactions, or of complexes with disordered regions, remains challenging. Nuclear Magnetic Resonance (NMR) spectroscopy offers atomic-level insights into biomolecular complexes, even in weakly interacting and dynamic systems. Paramagnetic NMR, in particular, provides long-range structural restraints, easily exceeding distances over 25 Å, making it ideal for studying large protein complexes. Advances in chemical tools for introducing paramagnetic tags into proteins, combined with progress in electron paramagnetic resonance (EPR) spectroscopy, have enhanced the method's utility. This perspective article discusses paramagnetic NMR approaches for analyzing biomolecular complexes in solution and in the solid state, emphasizing quantities like pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. Additionally, dynamic nuclear polarization offers a promising method to amplify NMR signals of large complexes, even in complex environments. The integration of AlphaFold protein structure prediction with paramagnetic NMR holds great potential for advancing our understanding of biomolecular interactions.

Catalytic Enantioselective Synthesis of Monosubstituted [n]Paracyclophane using a Desymmetrization/Kinetic Resolution Sequence.

Optically pure monosubstituted [n]paracyclophanes are promising candidates for material synthesis, asymmetric catalysis, and drug discovery. Thus far, only a few catalytic asymmetric synthesis processes have been reported for assessing these stained atropisomers. In this study, we describe a highly enantioselective synthesis of monosubstituted [n]paracyclophanes by combining desymmetrization and kinetic resolution. The proposed protocol involves Pd-catalyzed atroposelective C-H bond olefination enabled by a monoprotected amino acid ligand, which affords a variety of monosubstituted [n]paracyclophanes with excellent enantioselectivity (≥99% ee). Thermodynamic experiments are conducted to investigate the racemization barrier. The synthesized monosubstituted [n]paracyclophanes exhibit vivid fluorescence and impressive circularly polarized luminescence. Further, the emission color and dissymmetry factor can be tuned and controlled by switching the substituent on the benzene ring of the cyclophanes.

Peptide-Bismuth Tricycles: Maximizing Stability by Constraint.

Constrained peptides possess excellent properties for identifying lead compounds in drug discovery. While it has become increasingly straightforward to discover selective high-affinity peptide ligands, especially through genetically encoded libraries, their stability and bioavailability remain significant challenges. By integrating macrocyclization chemistry with bismuth binding, we generated series of linear, cyclic, bicyclic, and tricyclic peptides with identical sequences. Utilizing bismuth to rigidify the peptide structure allows for a better comparison of different constraint levels, reducing confounding effects of interactions often seen with hydrophobic stapling reagents. Our study facilitated the identification of a peptide-bismuth tricycle that fully withstands cellular levels of glutathione, acts as a nanomolar protease inhibitor without being proteolytically digested by its target, and is fully stable in human plasma. Importantly, this multicyclic peptide does not possess any non-canonical amino acid modifications. Using oxime ligation, we conjugated an analogue of this tricycle to the N-terminus of two nanobodies to demonstrate potential applications in targeted therapy.

DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks

Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework’s efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features.

Novel perspectives on the pharmacological treatment of thyroid-associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO), an autoimmune disease closely related to thyroid dysfunction, remains a challenging ophthalmic condition among adults. Its clinical manifestations are complex and diverse, and disease progression can lead to exophthalmos, diplopia, exposure keratitis, corneal ulceration, and compressive optic neuropathy, resulting in irreversible vision damage or even blindness. Traditional treatment methods for TAO, including glucocorticoids, immunosuppressants, and radiation therapy, often have limitations and side effects, making this disease problematic in ophthalmology. As a result, the development of novel targeted drugs has become a research hotspot for addressing the pathogenesis of TAO. A range of novel targeted drugs, such as teprotumumab and tocilizumab, have been successfully developed and demonstrated remarkable efficacy in relieving inflammation and managing this disease. In addition, some drug candidates and molecular targets identified in the TAO in vitro model have shown promising prospects. This article briefly reviews the potential new strategies for future clinical treatment and the progress of new drug therapies for TAO.

Exploring target selectivity in designing and identifying PI3Kα inhibitors for triple negative breast cancer with fragment-based and bioisosteric replacement approach

Triple-negative breast cancer (TNBC) is one of the most fatal malignancies in the world, accounting for 42% of all deaths due to metastasis. The significant development is hindered by the multi-drug resistance and poor patient compliance. PIK3CA gene mutation is one of the important causes of TNBC, which causes dysregulation of the cell cycle and cell proliferation. PI3Kα selective inhibition can decrease the TNBC by a significant level with minimal off-target effects. Novel compounds with high selectivity towards PI3Kα are crucial for treating TNBC. After extensive literature analysis, it was observed that fragment-based drug discovery, combined with structure-based virtual screening and bioisosteric replacement strategy, could provide a novel way for hit-to-lead optimization. The present study focussed on the fragment-based direct linking of 11269 moieties of the ChemDiv fragment library, - to generate novel moieties and further screened them using molecular docking, MMGBSA, and target selectivity analysis. Further, the top 2 moieties – Djh1 and Djh2 were selected after MMGBSA analysis and target selectivity prediction towards kinase. Further induced fit docking (IFD) analysis, DFT analysis, and MD simulation were employed to establish that – Djh1 and Djh2 could act as potential hit molecules for selective inhibition of PI3Kα. Further bioisosteric replacement, docking analysis, and target selectivity analysis were performed with the bioisosteres. The top two bioisosteres of Djh1 – Compound 10, Compound 06 represented excellent efficacy and selectivity towards PI3Kα in the treatment of TNBC after analysis of ADMET analysis. Further, in vitro and in vivo analysis might prove the effectiveness of the hit compounds.

Defining proteoform-specific interactions for drug targeting in a native cell signalling environment.

Understanding the dynamics of membrane protein-ligand interactions within a native lipid bilayer is a major goal for drug discovery. Typically, cell-based assays are used, however, they are often blind to the effects of protein modifications. In this study, using the archetypal G protein-coupled receptor rhodopsin, we found that the receptor and its effectors can be released directly from retina rod disc membranes using infrared irradiation in a mass spectrometer. Subsequent isolation and dissociation by infrared multiphoton dissociation enabled the sequencing of individual retina proteoforms. Specifically, we categorized distinct proteoforms of rhodopsin, localized labile palmitoylations, discovered a Gβγ proteoform that abolishes membrane association and defined lipid modifications on G proteins that influence their assembly. Given reports of undesirable side-effects involving vision, we characterized the off-target drug binding of two phosphodiesterase 5 inhibitors, vardenafil and sildenafil, to the retina rod phosphodiesterase 6 (PDE6). The results demonstrate differential off-target reactivity with PDE6 and an interaction preference for lipidated proteoforms of G proteins. In summary, this study highlights the opportunities for probing proteoform-ligand interactions within natural membrane environments.

Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders

Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. The performance evaluation of these models demonstrated SVM as the best model, which was further utilized to shortlist 51 highly potent compounds by screening an FDA-approved library of 1040 drugs. Molecular docking and molecular dynamic simulation were subsequently employed to evaluate the binding affinity and stability of the proposed compounds. Orlistat, acarbose and afatinib were identified as the potent leads, demonstrating stable conformations and stronger interactions, validated by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bond analyses. Molecular Mechanics/Generalized Born Surface Area (MMGBSA) analysis validated their binding affinities by providing comparably lower binding free energies for orlistat (− 33.4743 ± 3.8908), acarbose (− 19.5455 ± 5.4702), and afatinib (− 36.4944 ± 5.4929) than the control, dasatinib (− 13.7785 ± 5.8058). Finally, toxicity analysis revealed orlistat and acarbose as the possible safer therapeutics by eliminating afatinib as it showed significant toxicity concerns. Our investigation supports the advance computational methods utilization in the field of drug discovery and suggest further experimental validation of proposed inhibitors of Src kinase for their safer use against inflammatory diseases. The ultimate aim of this study is to advance the development of effective treatments for inflammatory diseases, linked with Src overexpression.

Chemical Space of Fluorinated Nucleosides/Nucleotides in Biomedical Research and Anticancer Drug Discovery

Fluorinated nucleos(t)ide drugs have proven to be successful chemotherapeutic agents in treating various cancers. The Food and Drug Administration (FDA) has approved several drugs that fit within the fluorinated nucleoside pharmacophore, and many more are either in preclinical development or clinical trials. The addition of fluorine atoms to nucleos(t)ides improves the metabolic stability of the glycosidic bond and, in certain instances, facilitates additional interactions of nucleons(t)ides with receptors. The insertion of fluorine either on sugar or the base of nucleos(t)ides proved to enhance the lipophilicity, pharmacokinetic, and pharmacodynamic properties. Overall, the fluorine atom feeds diverse advantages to the biological profile of nucleos(t)ide analogs by improving their drug-like properties and therapeutic potential. This review article covers the often-used fluorinating reagents in nucleoside chemistry, the clinical significance of [18F]-labeled nucleosides, the synthesis and anticancer activity of FDA-approved fluoro-nucleos(t)ide drugs, as well as clinical candidates, which are at various stages of clinical development as anticancer agents.

Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks.

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety. To predict interactions between drugs used to treat these two diseases, we propose a method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, we map feature vectors representing drugs in different feature spaces to the same. Second, we propose drug relation regularization to determine drug pair relationships in the interaction space. Experimental results demonstrate that MDFLDRR can be effectively applied to two DDI prediction goals: predicting unobserved interactions among drugs within the drug network and predicting interactions between drugs inside and outside the network. Publicly available evidence confirms that MDFLDRR can accurately identify DDIs between cardiovascular drugs and antidepressants. Lastly, by utilizing drug structure calculations, we ascertained the severity of newly discovered DDIs to mine the potential co-medication risks and aid in the smart management of pharmaceuticals.

UBE2N: Hope on the Cancer Front, How to Inhibit This Promising Target Prospect?

UBE2N protein belongs to the UE2s family and plays a crucial role in DNA repair, making it an exciting target for the development of innovative anticancer therapies. With the aim of discovering UBE2N inhibitors (UBE2Ni), this perspective seeks to review and provide elements to guide the design of new compounds. We propose a chemoinformatic structural analysis of the protein and its areas of interaction with its different partners. While covalent UBE2Ni are the most advanced molecules in their development, noncovalent inhibitors offer significant advantages that could overcome the limitations of covalent ones, particularly in terms of selectivity. Lastly, to obtain a drug candidate, early assessment of the druggability of compounds is essential in a hit to lead process. For existing UBE2Ni, a critical challenge lies in their pharmacokinetic properties and will obviously have to be considered as early as possible to hope for an application in human therapy.

PairMap: An Intermediate Insertion Approach for Improving the Accuracy of Relative Free Energy Perturbation Calculations for Distant Compound Transformations.

Accurate prediction of the difference in binding free energy between compounds is crucial for reducing the high costs associated with drug discovery. Relative binding free energy perturbation (RBFEP) calculations are effective for small structural changes; however, large topological changes pose significant challenges for calculations, leading to high errors and difficulties in convergence. To address such issues, we propose a new approach─PairMap─that focuses on introducing appropriate intermediates for complex transformations between two input compounds. PairMap-generated intermediates exhaustively, determined the optimal conversion paths, and introduced thermodynamic cycles into the perturbation map to improve accuracy and reduce computational cost. PairMap succeeded in introducing appropriate intermediates that could not be discovered by existing simple approaches by comprehensively considering intermediates. Furthermore, we evaluated the accuracy of the prediction of binding free energy using 9 compounds selected from Wang et al.'s benchmark set, which included particularly complex transformations. The perturbation map generated by PairMap achieved excellent accuracy with a mean absolute error of 0.93 kcal/mol compared to 1.70 kcal/mol when using the perturbation map generated by the conventional Flare FEP intermediate introduction method. Moreover, in a scaffold hopping experiment conducted with the PDE5a target involving complex transformations, PairMap provided more accurate free energy predictions than ABFEP calculations, yielding more reliable results compared to experimental data. Additionally, PairMap can be utilized to introduce intermediates into congeneric series, demonstrating that complex links on the perturbation map can be resolved with minimal addition of intermediates and links. In conclusion, PairMap overcomes the limitations of existing methods by enabling RBFEP calculations for more complex transformations, further streamlining lead optimization in drug discovery.

Developing novel Lin28 inhibitors by computer aided drug design

Lin28 is a key regulator of cancer stem cell gene network that promotes therapy-resistant tumor progression in various tumors. However, no Lin28 inhibitor has been approved to treat cancer patients, urging exploration of novel compounds as candidates to be tested for clinical trials. In this contribution, we applied computer-aided drug design (CADD) in combination with quantitative biochemical and biological assays. These efforts led to the discovery of Ln268 as a drug candidate that can block Lin28 from binding to its RNA substrates and inhibit Lin28 activities. Ln268 suppressed Lin28-mediated cancer cell proliferation and spheroid growth. Results from nuclear magnetic resonance spectroscopy confirmed that Ln268 perturbs the conformation of the zinc knuckle domain of Lin28, validating the rational drug design by CADD. The inhibitory effects of Ln268 are dependent on Lin28 protein expression in cancer cells, highlighting limited off-target effects of Ln268. Moreover, Ln268 synergizes with several chemotherapy drugs to suppress tumor cell growth. In summary, Ln268 is a promising candidate for further development to target Lin28 as a cancer therapy.

FragMAX Facility for Crystallographic Fragment and Ligand Screening at MAX IV

ABSTRACTThe FragMAX facility at MAX IV Laboratory is a state‐of‐the‐art platform for crystallographic fragment screening, designed to support structure‐based drug and chemical tool compound discovery. This facility offers a comprehensive workflow, from high‐throughput crystal preparation and automated diffraction data collection at the BioMAX beamline to advanced data processing and analysis using custom software tools like FragMAXapp and FragMAXproc. Key components include an extensive relational SQLite database, various fragment libraries, laboratory automation equipment, and a range of bespoke software solutions. FragMAX has conducted numerous successful screening campaigns, serving both academic and industrial users. Users benefit from comprehensive support, and stringent data management. Here, we provide an overview of the different components of the facility and details of their practical implementation.

Unravelling the anti-inflammatory activity of Cyperus rotundus essential oil through gas chromatography-mass spectrometry analysis, network pharmacology, and molecular docking approaches

Cyperus rotundus rhizome is used in the traditional system of medicine to treat various diseases. The rhizomes of this plant are traditionally used as medicine for treatments of stomach pain, bowel disorders, and inflammatory pain. The present study aims to characterize the chemical constituents of the C. rotundus rhizomes essential oil (CREO) and to evaluate its possible mechanism of action as an anti-inflammatory agent by an integrative approach of gas chromatography-mass spectrometry (GC-MS), network pharmacology, and molecular docking analysis. The compound-target-disease network revealed cubenol, gamma murolene, cyperotundone, delta selinene, alpha copaene, alpha pinene, and beta caryophyllene are core compounds with higher degree values. Protein-protein interaction analysis revealed IL1B, IL10, IL6, PTGS2, TNF, and STAT3 as hub targets. A total of 1000 biological processes, 142 cellular components, and 241 molecular functional pathways were enriched. Molecular docking analysis revealed that hub compounds and protein targets had strong binding affinity between them. The top two docked poses with the lowest binding energy were identified as PTGS2-Cubenol and IL10-Gamma murolene with binding energies of -7.9 and -7.2 kcal/mol, respectively. A molecular dynamics study revealed that the PTGS2-Cubenol and IL10-Gamma murolene complex had a good amount of deformability. These results demonstrated that CREO can act on numerous proteins and pathways to form a systematic pharmacological network, and they can be considered as a candidate drug for treating inflammatory-related disorders