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Showing 1–17 of 17 results
Advanced filters: Author: Francesca Grisoni Clear advanced filters

  • Artificial Intelligence (AI) is accelerating drug discovery. Here the authors introduce a new approach to de novo molecule design - structured state space sequence models - to further extend AI’s capabilities of charting the chemical universe.

    • Rıza Özçelik
    • Sarah de Ruiter
    • Francesca Grisoni
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-12

  • A foundation model leverages large-scale medical knowledge to repurpose drugs for diseases that currently lack approved treatments, and provides explanations to support clinicians’ decisions.

    • Alaa Bessadok
    • Francesca Grisoni
    News & Views
    Nature Medicine
    Volume: 30, P: 3422-3423

  • Molecular representations based on SMILES strings play a pivotal role in the success of generative approaches for de novo design, however capturing information on synthetic accessibility remains challenging. Here, the authors report fragSMILES as a fragment-based molecular representation in strings that embed chemical information and molecular chirality, showing promise in generating molecules with desirable properties.

    • Fabrizio Mastrolorito
    • Fulvio Ciriaco
    • Orazio Nicolotti
    ResearchOpen Access
    Communications Chemistry
    Volume: 8, P: 1-9

  • Active deep learning is a promising approach to learn from low-data scenarios in drug discovery. This study illuminates key success factors of active learning and shows that it can boost hit discovery by up to sixfold over traditional methods.

    • Derek van Tilborg
    • Francesca Grisoni
    Research
    Nature Computational Science
    Volume: 4, P: 786-796

  • Combining machine learning with high-throughput synthesis expedites ionizable cationic lipid development for nanoparticle-based messenger RNA delivery.

    • Roy van der Meel
    • Francesca Grisoni
    • Willem J. M. Mulder
    News & Views
    Nature Materials
    Volume: 23, P: 880-881

  • Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

    • Kenneth Atz
    • Francesca Grisoni
    • Gisbert Schneider
    Reviews
    Nature Machine Intelligence
    Volume: 3, P: 1023-1032

  • With the aid of deep learning, the space of chemical molecules, such as candidates for drugs, can be constrained to find new bioactive molecules. A new open source tool can generate libraries of novel molecules with user defined properties.

    • Michael Moret
    • Lukas Friedrich
    • Gisbert Schneider
    Research
    Nature Machine Intelligence
    Volume: 2, P: 171-180

  • 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

  • Drug discovery has recently profited greatly from the use of deep learning models. However, these models can be notoriously hard to interpret. In this Review, Jiménez-Luna and colleagues summarize recent approaches to use explainable artificial intelligence techniques in drug discovery.

    • José Jiménez-Luna
    • Francesca Grisoni
    • Gisbert Schneider
    Reviews
    Nature Machine Intelligence
    Volume: 2, P: 573-584

  • Cytokines are key regulators of the immune system and can be recombinantly designed as therapeutics for immune-related disorders. However, the severe toxicity of recombinant cytokines limits their clinical translation. In this Review, the authors highlight bioengineering approaches for the design of clinically applicable and safe cytokine-based therapeutics.

    • Jeroen Deckers
    • Tom Anbergen
    • Willem J. M. Mulder
    Reviews
    Nature Reviews Bioengineering
    Volume: 1, P: 286-303

  • Artificial intelligence approaches to medicinal chemistry are increasingly powerful but struggle to predict bioactive molecules. Here a machine learning model generates synthetically accessible mimetics of natural products, which are shown to be bioactive against the retinoid X receptor.

    • Daniel Merk
    • Francesca Grisoni
    • Gisbert Schneider
    ResearchOpen Access
    Communications Chemistry
    Volume: 1, P: 1-9

  • The successful prediction of drug-like structures by scaffold hopping can be limited by the structural complexity of natural products. Here, a molecular descriptor which captures partial charge, atom density distributions, and molecular shape is used to predict novel active compounds which are simpler than the original natural products.

    • Francesca Grisoni
    • Daniel Merk
    • Gisbert Schneider
    ResearchOpen Access
    Communications Chemistry
    Volume: 1, P: 1-9