Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and...

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Pharmacology

Background:

• Artificial intelligence (AI) is revolutionizing drug discovery.
• Coumarin derivatives offer diverse pharmacological activities and structural adaptability for AI exploration.
• A unified synthesis of AI and coumarin chemistry workflows is currently lacking.

Purpose of the Study:

• To provide a comprehensive content framework by analyzing literature from the past ten years.
• To evaluate data-driven approaches that enhance coumarin-target discovery.
• To emphasize AI-enabled workflows connecting structural, functional, and phenotypic data for target identification and validation.

Main Methods:

• Literature review and analysis of the past ten years of research.
• Evaluation of data-driven approaches including data mining, molecular docking, predictive modeling, deep learning, and multiomics integration.
• Focus on AI-enabled workflows for hypothesis generation, target prioritization, and validation.

Main Results:

• AI-assisted algorithms accurately predict coumarin-protein interactions and uncover new biological targets.
• Deep learning and risk-benefit models have improved target ranking.
• Multiomics data fusion has revealed disease-specific mechanisms across various disorders, leading to novel drug designs.

Conclusions:

• The integration of AI and coumarin chemistry represents a paradigm shift in therapeutic target identification.
• AI-driven coumarin research is crucial for developing next-generation precision therapeutics.
• Future directions include ethical data governance, interpretability, and cross-disciplinary collaboration.