Open data and algorithms for open science in AI-driven molecular informatics
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Abstract
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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- Authors
- Brinkhaus, H. O., Rajan, K., Schaub, J., Zielesny, A., & Steinbeck, C.
- Affiliations
- Friedrich Schiller University Jena (DE)
- Corresponding author
- Steinbeck, Christoph
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 79
- Pages
- 102542
- ISSN
- 0959-440X (ISSN-L)
- OpenAlex
- W4321274994
- Cited by
- 29
- References
- 12
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 6 · 2023: 5
- FWCI
- 0.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Structural Biology (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Common Library Network (Der Gemeinsame Bibliotheksverbund) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (CRC1127)
- Carl-Zeiss-Stiftung
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- open data, open science, molecular informatics, FAIR data, open source software, deep learning, synthetic chemistry, chemical information extraction, natural product drug discovery, research data management, cloud computing
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Software, Informatics
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