CELFULL's AI Network Pharmacology Platform is at the forefront of aging intervention research. Using advanced natural language processing (NLP) methods, we have created a comprehensive database of anti-aging substances by analyzing extensive public literature sources. This database is the backbone of our AI model, trained to identify molecules with anti-aging properties based on specific criteria.
Our approach employs a data-driven AI methodology for virtual screening of potential anti-aging molecules. These candidates are then rigorously tested for their biological effects on aging through detailed cell experiments and animal trials. This AI-based screening process for anti-aging substances is not only more efficient but also more cost-effective compared to traditional biological experimentation methods.
The platform houses a vast database that includes:
This rich database enables us to construct sophisticated AI algorithm models for screening anti-aging substances and predict new aging-related targets from data networks like multi-omics data and substance-target interaction data. The aim is to identify potential anti-aging targets and assess their impact on the aging process through rigorous cell or animal experiments.
Our AI Network Pharmacology Platform includes:
AI Network Pharmacology Profiler
Genome Profiler
Protein Dynamics Profiler
Computing Biology Profiler
These profilers and algorithms facilitate the analysis of over 1.6 billion interactions between molecules and protein targets, encompassing 750 million substances and 9.6 million proteins from 2000 species.
