Ensembits is the first tokenizer of protein conformational ensembles that outperforms static tokenizers on RMSF prediction and matches them on function and mutation tasks while using less pretraining data.
Structure language models for protein conformation generation
3 Pith papers cite this work. Polarity classification is still indexing.
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ConforNets use channel-wise affine transforms on pre-Pairformer pair latents in OpenFold3 to achieve state-of-the-art unsupervised generation of alternate protein states and supervised conformational transfer across families.
DynaProt predicts per-residue 3x3 covariance matrices for local flexibility and scalar pairwise covariances for dynamic coupling from static protein structures using an SE(3)-invariant Gaussian framework, achieving high RMSF accuracy with far fewer parameters than prior methods.
citing papers explorer
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ENSEMBITS: an alphabet of protein conformational ensembles
Ensembits is the first tokenizer of protein conformational ensembles that outperforms static tokenizers on RMSF prediction and matches them on function and mutation tasks while using less pretraining data.
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ConforNets: Latents-Based Conformational Control in OpenFold3
ConforNets use channel-wise affine transforms on pre-Pairformer pair latents in OpenFold3 to achieve state-of-the-art unsupervised generation of alternate protein states and supervised conformational transfer across families.
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Learning residue level protein dynamics with multiscale Gaussians
DynaProt predicts per-residue 3x3 covariance matrices for local flexibility and scalar pairwise covariances for dynamic coupling from static protein structures using an SE(3)-invariant Gaussian framework, achieving high RMSF accuracy with far fewer parameters than prior methods.