About this Research Topic
Biological activity-guided or chromatographic feature-oriented screening strategies are widely used for canonical natural product discovery. However, these methods exhibit several limitations over time, including significant time and labor investment, frequent isolation of high-abundance natural products, and a consequent slowed pace of new natural product discovery.
Genomic information contains enormous chemical potential. With the increasing amount of data available, genome mining is gradually becoming a powerful tool for novel natural product discovery, providing crucial support for drug discovery.
In recent years, guided by emerging interdisciplinary means such as evolutionary theory, self-resistance mechanisms and artificial intelligence, the data-driven multidisciplinary genome mining workflow helps overcome some disadvantages inherent in the canonical strategy and discover novel bioactive molecules with high novelty and high efficiency.
The theme of this research topic revolves around the interdisciplinary applications of classical genome mining for natural products, such as artificial intelligence, evolutionary theory, and self-resistance mechanisms. Canonical natural product discovery is largely based on biological activity-guided or chromatographic feature-oriented screening strategies. However, insufficient sample biomass, low content of bioactives, and opportunistic attempts of repetitive work are common limiting factors. Genome mining guided by interdisciplinary applications has emerged as an emerging strategy with high novelty and efficiency. The aim of this special issue is to provide a data-driven paradigm for the discovery of new natural products from genetic data to bioactive molecules.
We welcome submissions of original research and review articles that focus on related field, but are not limited to, the following themes:
• Utilization of evolutionary theory in guided genome mining for novel structure discovery or higher efficient biosynthesis of natural products, involving investigation of gene families, biosynthetic pathways, regulatory network, and so on.
• Applying artificial intelligence to genome mining as an approach to screening large genomic databases and drug discovery with high throughput and accuracy.
• High-throughput genomic mining of bioactive natural products based on genetics of self-resistance mechanisms.
• Genome mining for natural products, enhanced or guided by any other interdisciplinary method, resulting in efficient integrated workflows.
Genomic information contains enormous chemical potential. With the increasing amount of data available, genome mining is gradually becoming a powerful tool for novel natural product discovery, providing crucial support for drug discovery.
In recent years, guided by emerging interdisciplinary means such as evolutionary theory, self-resistance mechanisms and artificial intelligence, the data-driven multidisciplinary genome mining workflow helps overcome some disadvantages inherent in the canonical strategy and discover novel bioactive molecules with high novelty and high efficiency.
The theme of this research topic revolves around the interdisciplinary applications of classical genome mining for natural products, such as artificial intelligence, evolutionary theory, and self-resistance mechanisms. Canonical natural product discovery is largely based on biological activity-guided or chromatographic feature-oriented screening strategies. However, insufficient sample biomass, low content of bioactives, and opportunistic attempts of repetitive work are common limiting factors. Genome mining guided by interdisciplinary applications has emerged as an emerging strategy with high novelty and efficiency. The aim of this special issue is to provide a data-driven paradigm for the discovery of new natural products from genetic data to bioactive molecules.
We welcome submissions of original research and review articles that focus on related field, but are not limited to, the following themes:
• Utilization of evolutionary theory in guided genome mining for novel structure discovery or higher efficient biosynthesis of natural products, involving investigation of gene families, biosynthetic pathways, regulatory network, and so on.
• Applying artificial intelligence to genome mining as an approach to screening large genomic databases and drug discovery with high throughput and accuracy.
• High-throughput genomic mining of bioactive natural products based on genetics of self-resistance mechanisms.
• Genome mining for natural products, enhanced or guided by any other interdisciplinary method, resulting in efficient integrated workflows.
Keywords: Genome mining, natural product, genetic data, prospecting
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
