Nationale Forschungsdateninfrastruktur (NFDI)

Hartl, Nathalie; Wössner, Elena; Sure-Vetter, York

doi:10.1007/s00287-021-01392-6

Cited by 26 publications

(17 citation statements)

References 2 publications

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“…Using the public cloud infrastructures will readily allow researchers to take advantage of the latest developments in hardware and software, which will lead to faster growth in the field and a reduction in energy consumption and space utilization. There are several initiatives working continuously to implement open data, open-source, and open science in their individual research area [13,14,17,18,20,21,62,91,92,103,104]. Fueled by the availability of more and more open research data, AI-powered molecular informatics will be a key driver of the digital transformation of chemistry in the coming years.…”

Section: Discussionmentioning

confidence: 99%

“…Journals and funders demanding the deposition of research data and the necessary establishment of suitable research data infrastructures will inevitably alleviate the data shortage problem in the future [15,16]. The German government, for example, has recently decided to implement and long-term-fund a national research data infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) [17] with 30 consortia in all areas of science, collaboratively developing open research data management (RDM) e-infrastructures, coordinated by an umbrella process and a joint directorate. One of those consortia is NFDI4Chem which is building an RDM e-infrastructure for chemistry that follows FAIR data principles [18] to make chemical data findable, accessible, interoperable, and reusable [19,20].…”

Section: Introductionmentioning

confidence: 99%

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

Brinkhaus

Rajan

Schaub

et al. 2022

Preprint

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Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. The success of AlphaFold led to a growing interest in applying deep learning to a number of 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 such as the FAIR data movement, open data, and 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 that support their studies. 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 algorithms 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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Brinkhaus

Rajan

Schaub

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…With these FAIR guidelines in mind, the German NFDI initiative (Nationale Forschungsdateninfrastruktur, national research data infrastructure) was founded in October 2020 in order to improve the interdisciplinary data exchange [19]. NFDI operates in different (up to 30) consortia (with 10-20 member institutions each) and its goal is to create a data infrastructure (base) for scientific data in order to make the data reusable for everyone, rather than being stored offline with only limited access.…”

Section: Existing Data Bases and Approachesmentioning

confidence: 99%

Development of an Ontology for Biocatalysis

Menke

Behr

Rosenthal

et al. 2022

Chemie Ingenieur Technik

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Enzyme activity data for biocatalytic applications are currently often not annotated with standardized conditions and terms. This makes it extremely hard to retrieve, compare, and reuse enzymatic data. With advances in the fields of artificial intelligence (AI) and machine learning (ML), the automated usability of data in the form of machine-readable annotations will play a crucial role for their success. It is becoming increasingly easy to retrieve complex data sets and extract relevant information; however, standardized data readability is a current limitation. In this contribution, we outline an iterative approach to develop standardized terms and create semantic relations (ontologies) to achieve this highly desirable goal of improving the discoverability, accessibility, interoperability, and reuse of digital resources in the field of biocatalysis.

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Section: Introductionmentioning

confidence: 99%

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

Brinkhaus

Rajan

Schaub

et al. 2023

Preprint

View full text Add to dashboard Cite

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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Nationale Forschungsdateninfrastruktur (NFDI)

Cited by 26 publications

References 2 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

Development of an Ontology for Biocatalysis

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

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