Research on highly concurrency payment systems has proposed distributed architectures with layered consistency controls to balance transaction accuracy and scalability. This framework improves response time, throughput, and reliability in large-scale digital payment environments by separating system components and applying adaptive consistency strategies.
— As artificial intelligence continues to reshape biomedical research, data-driven methods are opening new possibilities for understanding complex inflammatory diseases. In research papers Role and mechanism of deep statistical machine learning in biological target screening and immune microenvironment regulation of asthmadeep statistical machine learning has been explored as a tool for asthma-related target discovery and immune microenvironment research.
This study addresses a central challenge in natural product-based drug discovery: how to improve the rapid identification of promising lead compounds despite limited samples and structural complexity. Natural products exhibit remarkable biological activity and structural diversity, and asthma research is closely related to immunomodulation and inflammatory responses. This study explores a more systematic path to identifying relevant candidate compounds for the study of inflammatory diseases by combining computational screening and biological validation.
The focus of the study is PDE4 and PDE7, two enzymes identified in this paper with roles in inflammation-related mechanisms. This paper highlights PDE4/7 dual-target inhibitors as a potential research direction, especially since PDE4 inhibitors may be associated with side effects in clinical use. By focusing on these targets, this study explores computational paths to identify candidates for anti-inflammatory research with potential efficacy and safety benefits.
To identify potential candidates, this study combines computer-assisted drug discovery, deep learning, molecular docking, and experimental validation. This framework compares the crystal structures and major binding sites of PDE4 and PDE7 proteins, screens natural product compounds, evaluates molecular structure, and predicts inhibitory activity. Through this process, the study identified 179 potential small molecules that could interact with PDE4 and PDE7 and subsequently selected 16 natural compounds for further activity validation.
A key feature of this study is the use of an artificial neural network-based predictive model. This model was designed to predict the biological activity of target molecules based on the structural information and known IC50 values of PDE4 and PDE7 inhibitors. In this study, we also used molecular fingerprint-based similarity analysis to evaluate the structural properties of the screened compounds and provide a basis for further screening and structure optimization.
The study also went beyond computational predictions by incorporating biological validation. Candidate compounds were tested through PDE4 and PDE7 enzyme activity assays, cell-based experiments, and inflammatory factor analysis. In RAW264.7 cell experiments under LPS-induced inflammatory conditions, this study examined NO levels and measured IL-6 and TNF-α through ELISA tests. Multiple compounds showed inhibitory effects on PDE4 and PDE7 activities, providing preliminary support for further studies of natural product-based dual-target inhibitors.
The study’s author, Pengwei Zhu, has an interdisciplinary background in biostatistics, data science, quantitative modeling, healthcare analytics, and AI-related research. He is a PhD biostatistician and applied economist with experience applying statistical modeling and machine learning techniques to healthcare and data-driven research. His extensive research experience also includes research in the healthcare and AI industries across areas such as AI healthcare, innovative medicines, medical services, medical devices, health management, and industrial policy. This combination of biostatistics, AI modeling, and healthcare industry research provides a background for cross-disciplinary directions in research.
Zhu’s research provides a practical example of how AI-assisted research can support the search for early-stage treatments by combining deep learning, molecular screening, and experimental validation. Its importance extends beyond a single set of candidate compounds and points to broader research directions where machine learning can improve screening efficiency, prioritize candidate compounds, and support natural product-based drug discovery for inflammatory diseases such as asthma.
Contact information:
Name: Zhu Pengwei
Email: Send email
Organization: Zhu Pengwei
Website: https://scholar.google.com/quotes?hl=ja&user=-H7xKKsAAAAJ
Release ID: 89194603
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