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Showing 1–8 of 8 results
Advanced filters: Author: David F. Nippa Clear advanced filters

  • Efficient lead optimization in drug discovery requires improving potency, synthetic accessibility, and physicochemical properties. Here, the authors utilize machine learning to screen large chemical spaces, demonstrating automated selection of optimized molecules to improve cycle times.

    • David F. Nippa
    • Kenneth Atz
    • Gisbert Schneider
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • Late-stage functionalization of complex drug molecules is challenging. To address this problem, a discovery platform based on geometric deep learning and high-throughput experimentation was developed. The computational model predicts binary reaction outcome, reaction yield and regioselectivity with low error margins, enabling the functionalization of complex molecules without de novo synthesis.

    • David F. Nippa
    • Kenneth Atz
    • Gisbert Schneider
    ResearchOpen Access
    Nature Chemistry
    Volume: 16, P: 239-248

  • Scalable networks for processing and distribution of quantum information using photons can be achieved by using multiplexed quantum states. Here, the authors report frequency-multimode storage and spectral-temporal photon manipulation of heralded single photons at telecom wavelength, in a fully integrated setting.

    • Erhan Saglamyurek
    • Marcelli Grimau Puigibert
    • Wolfgang Tittel
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-7

  • The use of data-driven generative models for drug design is challenging due to the scarcity of data. Here, the authors introduce a “zero-shot" generative deep model to enable the generation of molecules by both structure- and ligand-based drug design and apply it to design PPARγ agonists with desired properties.

    • Kenneth Atz
    • Leandro Cotos
    • Gisbert Schneider
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-18

  • Late-stage functionalization of drug molecules can tune their properties without the need for entirely new syntheses, however, predicting reactivity and planning synthesis for late-stage C-H activation remains challenging. Here, the authors develop a reaction screening approach combining high-throughput experimentation with computational graph neural networks to identify suitable substrates that can be used for late-stage C-H alkylation via Minisci-type chemistry.

    • David F. Nippa
    • Kenneth Atz
    • Gisbert Schneider
    ResearchOpen Access
    Communications Chemistry
    Volume: 6, P: 1-11