The integration of bioinformatics, artificial intelligence (AI), and the molecular biology is transforming the field of natural product-based drug discovery. This combination allows for the efficient computational analysis of large multi-omics and other biological datasets, ultimately facilitating the discovery and optimization of novel biological compounds.
Natural products (NPs) have long been a source of therapeutic agents, and these advanced technologies enhance our ability to explore them. They enable a more streamlined process for identifying biosynthetic gene clusters in bacteria, plants, and fungi, for example; and for characterizing the complex biosynthetic pathways that produce these compounds, unlocking new ways to discover potential drug leads and our understanding of the mechanisms behind their synthesis.
Natural products have diverse therapeutic applications such as anticancer, antituberculosis, antibiotics and anti-biofilms. Despite the vast potential that NPs hold for yielding novel therapeutic agents, the process of identifying, isolating, and characterizing these compounds is notoriously complex, time-consuming, and resource-intensive. Traditional methods often lead to bottlenecks in screening and analysis, hindering the efficient exploration of NP diversity. To overcome these challenges, in this Topic we aim to showcase the leveraging of integrated bioinformatics, artificial intelligence (AI), and molecular biology, which together can significantly enhance the efficiency and effectiveness of natural product research and NB-based drug discovery.
This Research Topic welcomes original research articles, review manuscripts and methods articles covering, but not restricted to:
- NPs and microbiome
- AI in structure determination, structure prediction, and NP-based de novo drug design
- Foundation models of AI (e.g., ESM2) applied to Bioinformatics
- Generative AI methods for protein design (enzymes and peptides)
- The role of bioinformatics, AI, and molecular biology in the discovery of NPs
- Databases and computational tools
- Imaging and AI
- ADMET modeling of NPs.
Keywords:
Bioinformatics, Artificial Intelligence, machine Learning, Molecular Biology, Synthetic Biology, Natural Products, Drug Discovery, Drug Discovery in Bioinformatics, Integrative Bioinformatics
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.
The integration of bioinformatics, artificial intelligence (AI), and the molecular biology is transforming the field of natural product-based drug discovery. This combination allows for the efficient computational analysis of large multi-omics and other biological datasets, ultimately facilitating the discovery and optimization of novel biological compounds.
Natural products (NPs) have long been a source of therapeutic agents, and these advanced technologies enhance our ability to explore them. They enable a more streamlined process for identifying biosynthetic gene clusters in bacteria, plants, and fungi, for example; and for characterizing the complex biosynthetic pathways that produce these compounds, unlocking new ways to discover potential drug leads and our understanding of the mechanisms behind their synthesis.
Natural products have diverse therapeutic applications such as anticancer, antituberculosis, antibiotics and anti-biofilms. Despite the vast potential that NPs hold for yielding novel therapeutic agents, the process of identifying, isolating, and characterizing these compounds is notoriously complex, time-consuming, and resource-intensive. Traditional methods often lead to bottlenecks in screening and analysis, hindering the efficient exploration of NP diversity. To overcome these challenges, in this Topic we aim to showcase the leveraging of integrated bioinformatics, artificial intelligence (AI), and molecular biology, which together can significantly enhance the efficiency and effectiveness of natural product research and NB-based drug discovery.
This Research Topic welcomes original research articles, review manuscripts and methods articles covering, but not restricted to:
- NPs and microbiome
- AI in structure determination, structure prediction, and NP-based de novo drug design
- Foundation models of AI (e.g., ESM2) applied to Bioinformatics
- Generative AI methods for protein design (enzymes and peptides)
- The role of bioinformatics, AI, and molecular biology in the discovery of NPs
- Databases and computational tools
- Imaging and AI
- ADMET modeling of NPs.
Keywords:
Bioinformatics, Artificial Intelligence, machine Learning, Molecular Biology, Synthetic Biology, Natural Products, Drug Discovery, Drug Discovery in Bioinformatics, Integrative Bioinformatics
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.