Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation

Targeting JNK kinase inhibitors via molecular docking: A promising strategy to address tumorigenesis and drug resistance

Among members of the mitogen-activated protein kinase (MAPK) family, c-Jun N-terminal kinases (JNKs) are vital for cellular responses to stress, inflammation, and apoptosis. Recent advances have highlighted their important implications in cancer biology, where dysregulated JNK signalling plays a role in the growth, progression, and metastasis of tumors. The present understanding of JNK kinase and its function in the etiology of cancer is summarized in this review. By modifying a number of downstream targets, such as transcription factors, apoptotic regulators, and cell cycle proteins, JNKs exert diverse effects on cancer cells. Apoptosis avoidance, cell survival, and proliferation are all promoted by abnormal JNK activation in many types of cancer, which leads to tumor growth and resistance to treatment. JNKs also affect the tumour microenvironment by controlling the generation of inflammatory cytokines, angiogenesis, and immune cell activity. However, challenges remain in deciphering the context-specific roles of JNK isoforms and their intricate crosstalk with other signalling pathways within the complex tumor environment. Further research is warranted to delineate the precise mechanisms underlying JNK-mediated tumorigenesis and to develop tailored therapeutic strategies targeting JNK signalling to improve cancer management. The review emphasizes the role of JNK kinases in cancer biology, as well as their potential as pharmaceutical targets for precision oncology therapy and cancer resistance. Also, this review summarizes all the available promising JNK inhibitors that are suggested to promote the responsiveness of cancer cells to cancer treatment.

Combinational study with network pharmacology, molecular docking and preliminary experiments on exploring common mechanisms underlying the effects of weijing decoction on various pulmonary diseases

Objective

'Homotherapy for heteropathy' is a theory by which different diseases with similar pathogenesis can be treated with one Chinese formula. We aimed to explore the key components and core targets of Weijing decoction (WJD) in treating various lung diseases, namely, pneumonia, chronic obstructive pulmonary disease (COPD), acute lung injury (ALI), pulmonary fibrosis, pulmonary tuberculosis and non-small cell lung cancer (NSCLC), via network pharmacology, molecular docking and some experiments.

Significance

This is the first study on the mechanism of WJD in treating various lung diseases by 'homotherapy for heteropathy'. This study is helpful for the transformation of TCM formula and development of new drugs.

Methods

Active components and therapeutic targets of WJD were obtained via TCMSP and UniProt databases. Targets of the six pulmonary diseases were harvested from the GeneCards TTD, DisGeNet, UniProt and OMIM databases. Drug-disease intersection targets, corresponding Venn diagrams, herb-component-target networks and protein-protein interaction networks were established. Furthermore, GO biological function and KEGG enrichment analysis were completed. Moreover, the binding activity between main compounds and core targets was measured through molecular docking. Finally, the xenograft NSCLC mouse model was established. Immune responses were evaluated by flow cytometry and mRNA expression levels of critical targets were measured by real-time PCR.

Results

JUN, CASP3 and PTGS2 were the most critical targets in six pulmonary diseases. The active compounds beta-sitosterol, tricin and stigmasterol stably bound to many active sites on target proteins. WJD had extensive pharmacological regulation, involving pathways related to cancer, inflammation, infection, hypoxia, immunity and so on.

Conclusions

Effects of WJD against various lung diseases involve lots of compounds, targets and pathways. These findings will facilitate further research as well as clinical application of WJD.

Machine learning in TCM with natural products and molecules: current status and future perspectives

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

Identification of potential extracellular signal-regulated protein kinase 2 inhibitors based on multiple virtual screening strategies

The integration of multiple virtual screening strategies facilitates the balance of computational efficiency and prediction accuracy. In this study, we constructed an efficient and reliable "multi-stage virtual screening-in vitro biological validation" system to identify potential inhibitors targeting extracellular signal-regulated protein kinase 2 (ERK2). Firstly, we rapidly obtained 10 candidate ERK2 inhibitors with desirable pharmacokinetic characteristics from thousands of named natural products in ZINC database based on machine learning classification models and ADME/T prediction. The structure-based molecular docking approach was then used to obtain four further hits with lower binding free energy compared to the positive control molecule Magnolipin. Subsequently, the two compounds were purchased for in vitro biological validation considering commercial availability and economic cost, and the results showed that Dodoviscin A exhibited acceptable inhibitory activity on ERK2 (IC50 = 10.79 μm). Finally, the mechanism of action and binding stability of this natural product inhibitor were investigated by binding mode analysis and molecular dynamics simulation.