Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches

Area of Science:

• Pharmacology
• Cheminformatics
• Bioinformatics
• Computational Biology

Background:

• Osteoporosis is a common disease in the elderly.
• Traditional Chinese medicine (TCM) offers natural compounds with anti-osteoporosis potential.
• Exploring TCM mechanisms requires advanced computational methods due to complex ingredient profiles.

Purpose of the Study:

• To develop a high-throughput approach integrating cheminformatics and deep learning.
• To explore the mechanistic actions of Chinese herbs against osteoporosis.
• To identify herb/drug-gene interaction networks and novel active compounds.

Main Methods:

• Selected 10 medicinal herbs for osteoporosis treatment.
• Retrieved chemical ingredients and predicted targets, alongside clinical drug data.
• Analyzed chemical structures and similarities using RDKit and SKlearn.
• Constructed herb-ingredient-target networks and performed enrichment analyses (GO, KEGG).
• Predicted compound bioactivity and performed molecular docking.

Main Results:

• Identified a pharmacological network with 89 active components and 30 potential genes.
• Found steroids, flavonoids, and alkaloids to be key anti-osteoporosis chemical structures.
• Demonstrated that active herb components are involved in steroid hormone biosynthesis and TNF signaling pathways.
• Developed deep learning models to evaluate protein targets and predict compound activity.

Conclusions:

• The integration of cheminformatics and deep learning effectively elucidates TCM mechanisms.
• This approach facilitates the discovery of novel active compounds from medicinal herbs.
• It provides a powerful strategy for understanding complex molecular systems in drug discovery.