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Showing 1–12 of 12 results
Advanced filters: Author: Cristian G. Bologa Clear advanced filters
  • 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
  • A lack of robust knowledge of the number of rare diseases and the number of people affected by them limits the development of approaches to ameliorate the substantial cumulative burden of rare diseases. Here, we call for coordinated efforts to more precisely define rare diseases.

    • Melissa Haendel
    • Nicole Vasilevsky
    • Tudor I. Oprea
    Comments & Opinion
    Nature Reviews Drug Discovery
    Volume: 19, P: 77-78
  • 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
  • Critical Assessment of Computational Hit-finding Experiments (CACHE) is a public benchmarking project to compare and improve computational small-molecule hit-finding approaches through cycles of prediction, compound synthesis and experimental testing. By that, CACHE will enable a more efficient and effective approach to hit identification and drug discovery.

    • Suzanne Ackloo
    • Rima Al-awar
    • Timothy M. Willson
    Reviews
    Nature Reviews Chemistry
    Volume: 6, P: 287-295
  • Jessica Binder et al. developed a machine learning model to discover potential drug targets for Alzheimer’s disease. They validated their 20 top candidates in several in vitro models, and highlight FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2 as potential AD risk genes.

    • Jessica Binder
    • Oleg Ursu
    • Tudor I. Oprea
    ResearchOpen Access
    Communications Biology
    Volume: 5, P: 1-15
  • Clarke, Evangelista et al. use a knowledge graph (ReproTox-KG) to characterize associations between small molecule compounds and their potential to induce specific birth abnormalities. They identify over 500 birth defect/gene/drug connections that can explain molecular mechanisms for drug-induced birth defects.

    • John Erol Evangelista
    • Daniel J. B. Clarke
    • Avi Ma’ayan
    ResearchOpen Access
    Communications Medicine
    Volume: 3, P: 1-14