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Showing 1–4 of 4 results
Advanced filters: Author: Marwin Segler Clear advanced filters

  • Deep neural networks and Monte Carlo tree search can plan chemical syntheses by training models on a huge database of published reactions; their predicted synthetic routes cannot be distinguished from those a human chemist would design.

    • Marwin H. S. Segler
    • Mike Preuss
    • Mark P. Waller
    Research
    Nature
    Volume: 555, P: 604-610

  • Over their careers, medicinal chemists develop a gut feeling for what is a promising molecule. Here, the authors use machine learning models to learn this intuition and show that it can be successfully applied in several drug discovery scenarios.

    • Oh-Hyeon Choung
    • Riccardo Vianello
    • José Jiménez-Luna
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-10

  • Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.

    • Michael W. Mullowney
    • Katherine R. Duncan
    • Marnix H. Medema
    Reviews
    Nature Reviews Drug Discovery
    Volume: 22, P: 895-916

  • Studies employing machine-learning (ML) tools in the chemical sciences often report their evaluations in a heterogeneous way. The evaluation guidelines provided in this Perspective should enable more rigorous ML reporting.

    • Andreas Bender
    • Nadine Schneider
    • Tiago Rodrigues
    Reviews
    Nature Reviews Chemistry
    Volume: 6, P: 428-442