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Showing 1–2 of 2 results
Advanced filters: Author: Jacquelyn L. Klug-McLeod Clear advanced filters

  • The efficient separation of chiral molecules is a fundamental challenge in the manufacture of pharmaceuticals and light-polarising materials. Here, the authors develop an approach that combines machine learning with a physics-based representation to predict resolving agents for chiral molecules, using a transformer-based neural network.

    • Rokas Elijošius
    • Emma King-Smith
    • Alpha A. Lee
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-10

  • High-throughput experimentation (HTE) has great utility for chemical synthesis. However, robust interpretation of high-throughput data remains a challenge. Now, a flexible analyser has been developed on the basis of a machine learning-statistical analysis framework, which can reveal hidden chemical insights from historical HTE data of varying scopes, sizes and biases.

    • Emma King-Smith
    • Simon Berritt
    • Alpha A. Lee
    ResearchOpen Access
    Nature Chemistry
    Volume: 16, P: 633-643