ReviewJournal of molecular biology2026
The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.
Review in Journal of molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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23 authors.
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Abstract
The ability to understand proteins and their behaviors has been drastically improved by major successes in structure prediction and the appearance of Large Protein Language Models (LPLMs). The speed with which Deep Learning and Artificial Intelligence are now affecting computational protein studies is remarkable, but there are now many opportunities for further rapid progress with applications of these methods. Rapid gains are likely to come from studies using the approaches identified in this perspective. Addressing and predicting ligand-binding sites in protein structures, as well as the prediction of reliable structures of proteins interacting with other proteins, will be pivotal for learning the full details of structural mechanisms and dynamics. The prediction of multi-state protein ensembles, conformational transitions, dynamics of large protein complexes, and integration with experimental data is likely to happen quickly.
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