Artificial intelligence for natural product drug discovery

Area of Science:

• Computational biology and bioinformatics
• Drug discovery and development
• Natural product chemistry

Background:

• Computational omics technologies enable exploration of natural product diversity for drug discovery.
• Artificial intelligence (AI), including machine learning, advances computational drug design, aiding activity prediction and de novo design.
• Synergies between omics and AI offer powerful tools for identifying novel drug candidates from natural sources.

Purpose of the Study:

• To describe current and future synergies between computational omics and AI for natural product drug discovery.
• To highlight the potential of integrating these technologies for identifying drug candidates.
• To discuss challenges and strategies for realizing the full potential of these integrated approaches.

Main Methods:

• Leveraging computational omics for natural product data generation and analysis.
• Applying machine learning and AI algorithms for biological activity prediction.
• Utilizing de novo drug design approaches for molecular target optimization.
• Developing strategies for high-quality dataset curation for AI model training.

Main Results:

• Identification of novel drug candidates from natural product libraries.
• Enhanced prediction of biological activities for natural compounds.
• Facilitation of de novo design of potential therapeutics.
• Discussion of critical factors for successful AI implementation in natural product drug discovery.

Conclusions:

• The integration of computational omics and AI presents a transformative approach to natural product drug discovery.
• Addressing challenges related to data quality and algorithm validation is crucial for maximizing the impact of these synergistic technologies.
• Future research should focus on robust validation and the development of high-quality datasets to fully harness the potential of AI in this field.