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

Henning Otto Brinkhaus1, Kohulan Rajan1, Jonas Schaub1

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Artificial intelligence (AI) and deep learning are transforming molecular informatics, but open data access remains a challenge. Embracing open science is crucial for advancing AI in drug discovery and chemical research.

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Area of Science:

  • Molecular informatics
  • Artificial intelligence
  • Deep learning

Background:

  • Deep learning and AI are increasingly applied in molecular informatics for synthetic chemistry, information extraction, and drug discovery.
  • Current AI applications are limited by the lack of FAIR and open data for training and testing models.

Purpose of the Study:

  • To review the current status of open data and algorithms in molecular informatics.
  • To identify areas for improvement in open science practices within the field.

Main Methods:

  • Literature review of AI applications in molecular informatics.
  • Analysis of trends in open data, open-source software, and open science initiatives.
  • Examination of the impact of open science on AI-driven molecular informatics.

Main Results:

  • Growing adoption of AI and deep learning in molecular informatics.
  • Significant challenges persist regarding FAIR and open data availability.
  • Emergence of open science initiatives supporting data and software sharing.

Conclusions:

  • Open science practices, including FAIR data and open-source software, are essential for the continued growth of AI-driven molecular informatics.
  • Academic researchers can leverage open-source frameworks and cloud platforms for AI model development.
  • Future advancements depend on increased commitment to open data management and a culture of open science.