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Showing 1–17 of 17 results
Advanced filters: Author: G Ursu Clear advanced filters

  • Thoughtfully designed negative training datasets may hold the key to more robust machine learning models. Ursu et al. reveal how negative training data composition shapes antibody prediction models and their generalizability. Sometimes, the best way to get better is to train harder.

    • Wesley Ta
    • Jonathan M. Stokes
    News & Views
    Nature Machine Intelligence
    Volume: 7, P: 1192-1194

  • Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery, but its evaluation in absolute terms does not adequately reflect the spectrum of compound quality. Here, an intuitive and transparent quantitative measure of drug-likeness is proposed that attempts to capture the abstract notion of aesthetics in medicinal chemistry.

    • G. Richard Bickerton
    • Gaia V. Paolini
    • Andrew L. Hopkins
    Research
    Nature Chemistry
    Volume: 4, P: 90-98

  • An in vivo screen of small-molecule compounds to inhibit the mosquito-stage development of Plasmodium identified hits that can be incorporated into bed nets and led to effective parasite killing in the insect host.

    • Alexandra S. Probst
    • Douglas G. Paton
    • Flaminia Catteruccia
    ResearchOpen Access
    Nature
    Volume: 643, P: 785-793

  • Off-target effects in CRISPR screens for essential regulatory elements have not been systematically evaluated. Here the authors find Cas9 nuclease, CRISPRi/a each have distinct off-target effects, and that these can be accurately identified and removed using the GuideScan sgRNA specificity score.

    • Josh Tycko
    • Michael Wainberg
    • Michael C. Bassik
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-14

  • Samples of different body regions from hundreds of human donors are used to study how genetic variation influences gene expression levels in 44 disease-relevant tissues.

    • François Aguet
    • Andrew A. Brown
    • Jingchun Zhu
    ResearchOpen Access
    Nature
    Volume: 550, P: 204-213

  • The authors summarize the data produced by phase III of the Encyclopedia of DNA Elements (ENCODE) project, a resource for better understanding of the human and mouse genomes.

    • Federico Abascal
    • Reyes Acosta
    • Zhiping Weng
    ResearchOpen Access
    Nature
    Volume: 583, P: 699-710

  • The success of mechanism-based drug discovery depends on the definition of the drug target, but targets are often poorly defined in the literature. Here, Overington and colleagues present a comprehensive map of the molecular targets of approved drugs, and explore aspects including the footprint of target classes across disease areas, the success of privileged target families and drug target orthologues across standard model organisms.

    • Rita Santos
    • Oleg Ursu
    • John P. Overington
    Research
    Nature Reviews Drug Discovery
    Volume: 16, P: 19-34

  • In 2014, the Illuminating the Druggable Genome programme was launched to promote the exploration of currently understudied but potentially druggable proteins. This article discusses how the systematic collection and processing of a wide array of biological and chemical data as part of this programme has enabled the development of evidence-based criteria for tracking the target development level of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. It also highlights the nature of the unexplored therapeutic opportunities for major protein families.

    • Tudor I. Oprea
    • Cristian G. Bologa
    • Gergely Zahoránszky-Köhalmi
    Research
    Nature Reviews Drug Discovery
    Volume: 17, P: 317-332

  • The authors summarize the history of the ENCODE Project, the achievements of ENCODE 1 and ENCODE 2, and how the new data generated and analysed in ENCODE 3 complement the previous phases.

    • Federico Abascal
    • Reyes Acosta
    • Richard M. Myers
    Reviews
    Nature
    Volume: 583, P: 693-698