PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
PulseAugur coverage of PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences — every cluster mentioning PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences across labs, papers, and developer communities, ranked by signal.
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New method ensures physical validity in biomolecular structure prediction
Researchers have developed a novel method to improve the physical validity of biomolecular structures predicted by diffusion models like AlphaFold 3. The approach introduces two projection operators that are applied at …
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New Harmonic Torsional Diffusion Framework Enhances Protein-Ligand Docking
Researchers have developed Harmony, a new framework for protein-ligand flexible docking that explicitly accounts for the periodic geometry of angular variables. Unlike previous diffusion-based models that use generic Eu…
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New DeCAF framework speeds up biomolecular structure generation
Researchers have developed a new framework called DeCAF to accelerate the process of generating 3D biomolecular structures. This method distills existing all-atom cofolding models into more efficient flow maps, signific…