Cheminformatics articles from across Nature Portfolio

Cheminformatics is the use of computational and informational techniques to understand problems of chemistry, for instance in the in silico mapping of chemical space – the theoretical space occupied by all possible chemicals and molecules. Cheminformatics strategies are useful in drug discovery and other efforts where large numbers of compounds are being evaluated for specific properties.

Latest Research and Reviews

News and Comment

  • Comments & Opinion |

    Large language models such as GPT-4 have been approaching human-level ability across many expert domains. GPT-4 can accomplish complex tasks in chemistry purely from English instructions, which may transform the future of chemistry.

    • Andrew D. White
  • News & Views |

    Retrosynthesis has served as a playground for computer-aided design for many decades. Computer-aided methods are usually predicated on human-expert rules or learning algorithms that extract the rules from literature data. Now, an approach that bridges the gap between these computer-driven methods and the traditional, intuition-driven, ‘chalk board’ retrosynthetic methods is reported.

    • Inbal L. Eshel
    •  & Anat Milo
  • News & Views |

    Informatics approaches play an increasingly important role in accelerating the advances of modern materials science. A recent study reports the development of predictive machine learning models to guide the de novo design of through-space charge transfer polymers with full-colour-tunable emission.

    • Xiaolin Liu
    • , Chunlei Zhu
    •  & Ben Zhong Tang
  • Comments & Opinion |

    Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability will make machine learning for chemistry more inclusive and drive methodological progress.

    • Daniel Probst