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Showing 1–11 of 11 results
Advanced filters: Author: Dmytro S. Radchenko Clear advanced filters

  • Tummino et al. dock 74 million molecules against the human cannabinoid-1 receptor to find uM ligands. Optimization led to a nM agonist conferring analgesia with reduced side effects in mice, highlighting its potential as a pain therapeutic and the promise of a structure-based approach.

    • Tia A. Tummino
    • Christos Iliopoulos-Tsoutsouvas
    • Brian K. Shoichet
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-19

  • Combining conformal prediction machine learning with molecular docking, a method to efficiently screen multi-billion-scale libraries is developed, enabling the discovery of a dual-target ligand modulating the A2A adenosine and D2 dopamine receptors.

    • Andreas Luttens
    • Israel Cabeza de Vaca
    • Jens Carlsson
    ResearchOpen Access
    Nature Computational Science
    Volume: 5, P: 301-312

  • The docking of a 1.7 billion- versus a 99 million-molecule virtual library against β-lactamase revealed that the larger-sized library produced improved hit rates and potency along with an increased number of scaffolds.

    • Fangyu Liu
    • Olivier Mailhot
    • Brian K. Shoichet
    Research
    Nature Chemical Biology
    Volume: 21, P: 1039-1045

  • Fragment-based drug design is an efficient yet challenging approach for developing therapeutics. Here, the authors employ structure-based docking screens of vast fragment libraries to identify inhibitors of 8-oxoguanine DNA glycosylase, a difficult drug target implicated in cancer and inflammation.

    • Andreas Luttens
    • Duc Duy Vo
    • Jens Carlsson
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-16

  • The vast scale of emerging on-demand chemical collections presents a challenge for efficiently identifying promising drug candidates. Here, the authors develop a bottom-up computational strategy to first explore fragment space and then exploit the most promising scaffolds, successfully identifying diverse and potent BRD4 binders.

    • Álvaro Serrano-Morrás
    • Andrea Bertran-Mostazo
    • Xavier Barril
    ResearchOpen Access
    Communications Chemistry
    Volume: 8, P: 1-11

  • V-SYNTHES, a scalable and computationally cost-effective synthon-based approach to compound screening, identified compounds with a high affinity for CB2 and CB1 in a hierarchical structure-based screen of more than 11 billion compounds.

    • Arman A. Sadybekov
    • Anastasiia V. Sadybekov
    • Vsevolod Katritch
    Research
    Nature
    Volume: 601, P: 452-459

  • Crystal structures of the σ2 receptor are determined and used to perform a docking screen of nearly 500 million molecules, identifying σ2-selective ligands and providing insight into the role of σ2 in neuropathic pain.

    • Assaf Alon
    • Jiankun Lyu
    • Andrew C. Kruse
    Research
    Nature
    Volume: 600, P: 759-764

  • VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

    • Christoph Gorgulla
    • Andras Boeszoermenyi
    • Haribabu Arthanari
    Research
    Nature
    Volume: 580, P: 663-668

  • Deep generative neural networks are increasingly exploited for drug discovery, but often the majority of generated molecules are predicted to be inactive. Here, an optimized protocol for generative models with reinforcement learning is derived and applied to design potent epidermal growth factor inhibitors.

    • Maria Korshunova
    • Niles Huang
    • Olexandr Isayev
    ResearchOpen Access
    Communications Chemistry
    Volume: 5, P: 1-11