Journal: Nature Reviews Drug Discovery

Abbreviation

Nat Rev Drug Discov

Publisher

Nature

Journal Volumes

ISSN

1474-1776
1474-1784

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Publications1 - 10 of 19
  • Cognitive dysfunction in psychiatric disorders: characteristics, causes and the quest for improved therapy
    Item type: Review Article
    Millan, Mark J.; Agid, Yves; Brüne, Martin; et al. (2012)
    Nature Reviews Drug Discovery
  • Natural products in drug discovery: advances and opportunities
    Item type: Review Article
    Atanasov, Atanas G.; International Natural Product Sciences Taskforce; Ristow, Michael; et al. (2021)
    Nature Reviews Drug Discovery
    Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities. © 2021 Springer Nature Limited
  • Demystifying industry-academia collaboration
    Item type: Other Journal Item
    Gersdorf, Thomas; He, Vivianna Fang; Schlesinger, Ann; et al. (2019)
    Nature Reviews Drug Discovery
  • Automating drug discovery
    Item type: Journal Article
    Schneider, Gisbert (2018)
    Nature Reviews Drug Discovery
  • Interfering with pH regulation in tumours as a therapeutic strategy
    Item type: Review Article
    Neri, Dario; Supuran, Claudiu T. (2011)
    Nature Reviews Drug Discovery
  • Virtual screening: an endless staircase?
    Item type: Journal Article
    Schneider, Gisbert (2010)
    Nature Reviews Drug Discovery
    Computational chemistry — in particular, virtual screening — can provide valuable contributions in hit- and lead-compound discovery. Numerous software tools have been developed for this purpose. However, despite the applicability of virtual screening technology being well established, it seems that there are relatively few examples of drug discovery projects in which virtual screening has been the key contributor. Has virtual screening reached its peak? If not, what aspects are limiting its potential at present, and how can significant progress be made in the future?
  • Automated synthesis of oligosaccharides as a basis for drug discovery
    Item type: Review Article
    Seeberger, Peter H.; Werz, Daniel B. (2005)
    Nature Reviews Drug Discovery
  • Designing antimicrobial peptides
    Item type: Review Article
    Fjell, Christopher D.; Hiss, Jan A.; Hancock, Robert E.W.; et al. (2012)
    Nature Reviews Drug Discovery
  • Averting inflammation by targeting the cytokine environment
    Item type: Journal Article
    Kopf, Manfred; Bachmann, Martin F.; Marsland, Benjamin J. (2010)
    Nature Reviews Drug Discovery
  • Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
    Item type: Journal Article
    Tropsha, Alexander; Isayev, Olexandr; Varnek, Alexandre; et al. (2024)
    Nature Reviews Drug Discovery
    Quantitative structure–activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term ‘deep QSAR’. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
Publications1 - 10 of 19