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Showing 1–16 of 16 results
Advanced filters: Author: Daniel Reker Clear advanced filters

  • Active machine learning is employed in academia and industry to support drug discovery. A recent study unravels the factors that influence a deep learning models’ ability to guide iterative discovery.

    • Zachary Fralish
    • Daniel Reker
    News & Views
    Nature Computational Science
    Volume: 4, P: 727-728

  • Drugs that target peptide hormone receptors are of great interest in the treatment of type 2 diabetes. In spite of limited data and vast design spaces, a bespoke computational pipeline has designed peptides that target two receptors with high potency.

    • Chloe E. Markey
    • Daniel Reker
    News & Views
    Nature Chemistry
    Volume: 16, P: 1394-1395

  • Natural products provide a rich source of leads for drug discovery. Now, a computational method is available that can be used to identify the macromolecular targets of these compounds. Much like medicinal chemists' reasoning, the software infers target information by comparing the substructures with those of drugs and other natural products with known targets.

    • Daniel Reker
    • Anna M. Perna
    • Gisbert Schneider
    Research
    Nature Chemistry
    Volume: 6, P: 1072-1078

  • We show the evolution of a case of EGFR mutant lung cancer treated with a combination of erlotinib, osimertinib, radiotherapy and a personalized neopeptide vaccine targeting somatic mutations, including EGFR exon 19 deletion.

    • Maise Al Bakir
    • James L. Reading
    • Charles Swanton
    ResearchOpen Access
    Nature
    Volume: 639, P: 1052-1059

  • As computation is increasingly integrated into drug research and development, this Perspective analyzes company business models, funding and deals to provide unique insights into risks and opportunities in this quickly maturing industry, which aims to expedite the creation of life-saving therapeutics.

    • Chloe Markey
    • Samuel Croset
    • Daniel Reker
    Reviews
    Nature Computational Science
    Volume: 4, P: 96-103

  • Self-assembly of small drugs with organic dyes represents a facile route to synthesize nanoparticles with high drug-loading capability. Here the authors combine a machine learning approach with high-throughput experimental validation to identify which combinations of drugs and excipient lead to successful nanoparticle formation and characterize the therapeutic efficacy of two of them in vitro and in animal models.

    • Daniel Reker
    • Yulia Rybakova
    • Giovanni Traverso
    Research
    Nature Nanotechnology
    Volume: 16, P: 725-733

  • Biochemical and cellular assays are often plagued by false positive readouts elicited by nuisance compounds. A significant proportion of those compounds are aggregators. This Review discusses the basis for colloidal aggregation, experimental methods for detecting aggregates and analyses recent progress in computer-based systems for detecting colloidal aggregation with particular emphasis on machine learning [In the online version of this Review originally published, the graphical abstract image was incorrectly credited to ‘Reven T.C. Wurman / Alamy Stock Photo’ this has now been corrected].

    • Daniel Reker
    • Gonçalo J. L. Bernardes
    • Tiago Rodrigues
    Reviews
    Nature Chemistry
    Volume: 11, P: 402-418

  • Natural products are a prime source of innovative molecular fragments and privileged scaffolds for drug discovery and chemical biology. Advanced machine-learning approaches can help analyse and design synthetically accessible, natural-product-derived, compound libraries and provide insight into the high selectivity of such compounds.

    • Tiago Rodrigues
    • Daniel Reker
    • Gisbert Schneider
    Reviews
    Nature Chemistry
    Volume: 8, P: 531-541

  • The development of prodrugs — derivatives of active pharmaceutical ingredients (APIs) with little or no biological activity themselves that are converted into the API after administration — can address issues with properties of the API such as poor bioavailability. This article provides a holistic analysis of approved prodrugs and discusses trends in prodrug design, their indications, mechanisms of API release and the chemistry of promoieties added to APIs to form prodrugs.

    • Zachary Fralish
    • Ashley Chen
    • Daniel Reker
    Reviews
    Nature Reviews Drug Discovery
    Volume: 23, P: 365-380

  • 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