Benefiting from big data in natural products: importance of preserving foundational skills and prioritizing data quality

Cech, Nadja B.; Medema, Marnix H.; Clardy, Jon

doi:10.1039/d1np00061f

Cited by 16 publications

(9 citation statements)

References 25 publications

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“…The initial hope that large-scale data analysis in the different omics-related research fields would boost the drug discovery rate has not yet materialised [115], but the methods are progressing continuously. The open access to databases and repositories such as MetaboLights [116], the HMDB [29], the Metabolomics Workbench [117], and METASPACE [118] is crucial for the identification of metabolites and NP [112].…”

Section: Ai In Natural Product-based Drug Discoverymentioning

confidence: 99%

Open data and algorithms for open science in AI-driven molecular informatics

Brinkhaus

Rajan

Schaub

et al. 2023

Preprint

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Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data, as well as open-source software, have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

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Section: Ai In Natural Product-based Drug Discoverymentioning

confidence: 99%

Open data and algorithms for open science in AI-driven molecular informatics

Brinkhaus

Rajan

Schaub

et al. 2023

Preprint

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“…A big obstacle in the full-edged implementation of AI in NP research is the lack of integrated and curated databases. 198 Most of the data, such as taxonomic, structural, genomic, and metabolomic data, for the specic compounds are not available compiled in the form of databases and presented in the form of scientic literature, which is very difficult to access and analyze manually. 198,199 Hence, an integrated approach is required for the effective analysis of NPs, as is a single algorithm for the management of the entire process of NP discovery alone.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

“…198 Most of the data, such as taxonomic, structural, genomic, and metabolomic data, for the specic compounds are not available compiled in the form of databases and presented in the form of scientic literature, which is very difficult to access and analyze manually. 198,199 Hence, an integrated approach is required for the effective analysis of NPs, as is a single algorithm for the management of the entire process of NP discovery alone. By addressing these issues, the common problems associated with AI, such as errors and repeatability, can be controlled in the learning process from reliable datasets.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Artificial intelligence in microbial natural product drug discovery: current and emerging role

et al. 2022

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“…With the advent of affordable microbial genome sequencing, genome mining of natural products has become a powerful tool for natural product investigation [6] . The rapidly accumulating genome sequences now provide opportunities to reveal the global landscape of natural products by bioinformatic analysis [7] …”

Section: Introductionmentioning

confidence: 99%

Type II Polyketide Synthases: A Bioinformatics‐Driven Approach

Xie

Zhang

2023

ChemBioChem

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Bioinformatics has become an indispensable tool for natural products research in the genomic era. One of the key challenges is to accurately convert sequence data of a biosynthetic gene cluster into chemical information such as the enzymatic function or the biosynthetic product structure. Type II polyketide synthase is the most bioinformatically well‐studied class of non‐modular biosynthetic machinery and represents a model system to showcase bioinformatic applications in natural products research. This review takes a bioinformatics‐centered perspective and summarizes the past advances and future opportunities of bioinformatics‐guided research on type II polyketide synthases. How bioinformatics has contributed to deepen the chemical understanding of type II PKSs will be discussed with the focus on enzymology, evolution, structural prediction of the biosynthetic products, genome mining, and the global analyses of their polyketide products.

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Benefiting from big data in natural products: importance of preserving foundational skills and prioritizing data quality

Abstract: Big data is changing how we do natural products research and creating exciting new possibilities. Continued attention to enhancing data quality, increasing access, and preserving foundational skills is needed.

Cited by 16 publications

References 25 publications

Open data and algorithms for open science in AI-driven molecular informatics

Open data and algorithms for open science in AI-driven molecular informatics

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Type II Polyketide Synthases: A Bioinformatics‐Driven Approach

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