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Showing 1–6 of 6 results
Advanced filters: Author: Alain C. Vaucher Clear advanced filters
  • In organic chemistry, synthetic routes for new molecules are often specified in terms of reacting molecules only. The current work reports an artificial intelligence model to predict the full sequence of experimental operations for an arbitrary chemical equation.

    • Alain C. Vaucher
    • Philippe Schwaller
    • Teodoro Laino
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-11
  • Extracting experimental operations for chemical synthesis from procedures reported in prose is a tedious task. Here the authors develop a deep-learning model based on the transformer architecture to translate experimental procedures from the field of organic chemistry into synthesis actions.

    • Alain C. Vaucher
    • Federico Zipoli
    • Teodoro Laino
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-11
  • Herein, the authors develop a transformer-based language model to automate synthesis protocol extraction from heterogeneous catalysis literature. Embracing digital advances in catalysis demands a shift in data reporting norms, and they offer guidelines for writing protocols, which improve machine readability.

    • Manu Suvarna
    • Alain Claude Vaucher
    • Javier Pérez-Ramírez
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-11
  • Organic chemical reactions can be divided into classes that allow chemists to use the knowledge they have about optimal conditions for specific reactions in the context of other reactions of similar type. Schwaller et al. present here an efficient method based on transformer neural networks that learns a chemical space in which reactions of a similar class are grouped together.

    • Philippe Schwaller
    • Daniel Probst
    • Jean-Louis Reymond
    Research
    Nature Machine Intelligence
    Volume: 3, P: 144-152
  • Infrared spectroscopy stands out as an analytical tool for its affordability, simplicity, and accessibility, however, its use has been limited to the identification of a select few functional groups, as most peaks lie beyond human interpretation. Here, the authors use a transformer model that enables chemists to leverage all information contained within an IR spectrum to directly predict the molecular structure.

    • Marvin Alberts
    • Teodoro Laino
    • Alain C. Vaucher
    ResearchOpen Access
    Communications Chemistry
    Volume: 7, P: 1-11