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Embracing the Machine Learning Revolution in Chemistry

JBCS, vol. 36, No. 08, 2025

This special issue of the Journal of the Brazilian Chemical Society (JBCS) stands as a testament to the 
transformative power of machine learning (ML) within the chemical sciences. The diverse collection of papers presented herein highlights the profound impact and growing importance of ML in accelerating discoveries and advancing research across various sub-disciplines of 
chemistry.

From the intricate realm of strongly correlated systems, where machine learning is being employed to refine density functionals, to applications in medicinal chemistry and materials science, the versatility of these computational approaches is undeniable.

These advancements not only push 
the boundaries of what is possible in chemical research, but also underscore the innovative spirit driving the integration of artificial intelligence into our field. As highlighted by the insightful reviews in this special issue, ML models are proving instrumental in rationalizing large datasets, 
identifying subtle patterns and relationships that might elude traditional analysis. More critically, these advanced computational methods are enabling the accurate prediction of novel molecules and new materials with desired properties, significantly accelerating the discovery process and reducing the need for extensive experimental trial-and-error.

While numerous databases are currently available, there is still significant work to be done in expanding and enriching chemical data repositories. As highlighted 
in one of the reviews within this special issue, various strategies, such as Natural Language Processing (NLP), can be effectively developed to create more robust and comprehensive databases. With the aim of fostering a collaborative national database, the editors of this special issue will provide a platform for depositing  computational structures and properties (please, contact us). This initiative follows the principles of FAIR data (Findable, Accessible,
Interoperable, and Reusable)
and seeks to meet the growing demand from funding agencies and publications for data availability, ultimately promoting transparency and accelerating scientific discovery.

The contributions within this special issue collectively paint a clear picture: machine learning is no longer just a computational tool but an indispensable partner in 
contemporary chemical research. Its ability to extract insights from vast datasets, predict properties with unprecedented accuracy, and streamline complex analyses is fundamentally reshaping how we approach scientific inquiry. As we continue to navigate the complexities of chemical systems and material design, the methodologies showcased in this special issue will undoubtedly pave the way for exciting new discoveries and innovations.

Investing in skilled individuals and robust resources is paramount for advancing the area - without this crucial support, the vast potential of ML - assisted chemistry to optimize processes and predict molecular properties will remain untapped, hindering essential scientific and technological progress.

We extend our gratitude to all the authors and reviewers for their invaluable contributions to this special issue, which serves as a vibrant platform for sharing the latest advancements at the intersection of machine learning and chemistry. We are confident that these papers will inspire 
further research and foster collaborations, ultimately contributing to a deeper understanding and more rapid progression in the chemical sciences.

Mauricio D. Coutinho-Neto

Guest Editor of the Journal of the Brazilian Chemical Society

Centro de Ciências Naturais e Humanas (CCNH), Universidade Federal do ABC (UFABC), 09210-580 Santo André-SP, Brazil

https://orcid.org/0000-0002-7187-0076

Paula Homem-de-Mello

Executive Editor of the Journal of the Brazilian Chemical Society

Centro de Ciências Naturais e Humanas (CCNH), Universidade Federal do ABC (UFABC), 09210-580 Santo André-SP, Brazil

https://orcid.org/0000-0002-7049-4689

References
  1. Wilkinson, M. D.; Dumontier, M.; Aalbersberg, I. J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; Bonino da Silva Santos, L.; Bourne, P. E.; Bouwman, J.; Brookes, A. J.; Clark, T.; Crosas, M.; Dillo, I.; Dumon, O.; Edmunds, S.; Evelo, C. T.; Finkers, R.; Gonzalez-Beltran, A.; Gray, A. J. G.; Groth, P.; Goble, C.; Grethe, J. S.; Heringa, J.; Hoen, P. A. C. ’t; Hooft, R.; 
    Kuhn, T.; Kok, R.; Kok, J.; Lusher, S. J.; Martone, M. E.; Mons, A.; Packer, A. L.; Persson, B.; Rocca-Serra, P.; Roos, M.; van Schaik, R.; Sansone, S.-A.; Schultes, E.; Sengstag, T.; Slater, T.; Strawn, G.; Swertz, M. A.; Thompson, M.; van der Lei, J.; van Mulligen, E.; Velterop, J.; Waagmeester, A.; Wittenburg, P.; Wolstencroft, K.; Zhao, J.; Mons, B.; Sci. Data 2016, 3, 160018. [Crossref]

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