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Showing 1–5 of 5 results
Advanced filters: Author: Kieran Didi Clear advanced filters

  • Graphics processing unit-accelerated MMseqs2 offers tremendous speedups for homology retrieval from metagenomic databases, query-centered multiple sequence alignment generation for structure prediction, and structural searches with Foldseek.

    • Felix Kallenborn
    • Alejandro Chacon
    • Martin Steinegger
    ResearchOpen Access
    Nature Methods
    Volume: 22, P: 2024-2027

  • Designing antibodies and assessing their biophysical properties for potential therapeutic development is challenging with current computational methods. Ramon et al. have developed a deep learning approach called AbNatiV, based on a vector-quantized variational encoder that accurately assesses the nativeness of antibodies and nanobodies, which are small single-domain antibodies that have recently attracted considerable interest.

    • Aubin Ramon
    • Montader Ali
    • Pietro Sormanni
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 6, P: 74-91

  • A central concept for characterising phase-separating systems is the phase diagram but generation of such diagrams for biomolecular systems is typically slow and low-throughput. Here the authors describe PhaseScan, a combinatorial droplet microfluidic platform for high-resolution acquisition of multidimensional biomolecular phase diagrams.

    • William E. Arter
    • Runzhang Qi
    • Tuomas P. J. Knowles
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-10

  • This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems

    • Arne Schneuing
    • Charles Harris
    • Bruno Correia
    ResearchOpen Access
    Nature Computational Science
    Volume: 4, P: 899-909

  • MISATO is a database for structure-based drug discovery that combines quantum mechanics data with molecular dynamics simulations on ~20,000 protein–ligand structures. The artificial intelligence models included provide an easy entry point for the machine learning and drug discovery communities.

    • Till Siebenmorgen
    • Filipe Menezes
    • Grzegorz M. Popowicz
    ResearchOpen Access
    Nature Computational Science
    Volume: 4, P: 367-378