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

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, ac-ademic 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.

Automated summary

Recent years have witnessed a significant growth in the development of deep learning and AI-based molecular informatics. Although there is increasing interest in applying deep learning to various aspects of molecular informatics, the lack of FAIR and open data poses a constraint on the application of AI in this field. However, with the rise of open science practices and initiatives supporting open data and software, researchers in molecular informatics are encouraged to embrace open science and contribute to open repositories. The combination of open-source deep learning frameworks, cloud computing platforms, and a culture promoting open science provides opportunities for the continued growth of AI-driven molecular informatics.