Integrated Computational Framework for Multi-Dimensional Analysis of Natural Products

Integrated Computational Framework for Multi-Dimensional Analysis of Natural Products to Understand Whole Person Health Core Components of the Proposal: 1. Refinement of Stereochemical Catalogue: o Objective: Enhance the precision of structural data on natural products using advanced QM calculations. o Methods: Utilize high-level QM methods to predict and verify stereochemical configurations of complex natural molecules extracted from large datasets. o Data Sources: Integrate data from existing repositories, published literature, and possibly new high-throughput experimental data. 2. Development of an Integrated Computational Framework: o Objective: Create a robust computational system that integrates diverse data types (chemical, biological, clinical, and social data) to analyze the multifaceted impacts of natural products on health. o Methods:  Use AI and machine learning to correlate chemical properties of natural products with biological activities and health outcomes.  Develop multi-scale models that simulate the interactions of natural products across different biological systems.  Implement dimensionality reduction techniques to visualize complex, high-dimensional data. o Interoperability: Ensure that the computational tools developed are compatible across different data platforms and adhere to FAIR data principles. 3. Multi-Dimensional Data Analysis: o Objective: Analyze how combinations of natural products interact with multiple biological systems to influence whole person health. o Approach:  Aggregate and mine data from electronic health records (EHRs), omics repositories, social media, and other relevant sources.  Explore the impact of natural products on the gut microbiome and its subsequent effects on health.  Investigate the pleiotropic effects of natural products and their collective impact on health resilience and various health indicators (e.g., sleep, mood, immune function). 4. Collaborative and Transdisciplinary Team: o Composition: Include experts from data science, natural product chemistry, nutrition, clinical research, and bioinformatics. o Objective: Foster collaboration and knowledge exchange between diverse fields to innovate and enhance the analysis of natural product impacts.