Molecular insights fast-tracked: AI in biosynthetic pathway research

Area of Science:

• Biotechnology
• Pharmacology
• Agricultural Science

Background:

• Biosynthetic pathway research is vital for discovering bioactive natural products.
• Challenges exist in extracting molecular insights and optimizing pathways.
• Artificial intelligence (AI) offers potential solutions to these challenges.

Purpose of the Study:

• To review AI techniques applicable to biosynthetic pathway research.
• To explore AI applications in pathway discovery, design, and optimization.
• To discuss the integration of omics data with AI for enhanced research.

Main Methods:

• Overview of AI techniques: machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining.
• Discussion of AI applications in pathway discovery, design, and optimization.
• Analysis of benefits and challenges of integrating omics data and AI.

Main Results:

• AI significantly accelerates molecular insights in biosynthetic pathway research.
• AI facilitates pathway discovery, design, and optimization for natural products.
• Integration of omics data with AI enhances pathway research capabilities.

Conclusions:

• AI is transformative for biosynthetic pathway research, enabling rapid advancements.
• Synergy between AI and experimental approaches is crucial for future progress.
• AI presents new opportunities for discovering and optimizing bioactive natural products.