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#machine learning Preprint Oct 2026

Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features

Intrinsically disordered protein regions (IDRs) play central roles in cellular processes such as transcriptional regulation, signal transduction, and subcellular localization, yet their functional design remains challenging. Structure-based design methods do not readily apply to IDRs, and existing protein language mode...

J. Liu, Sebastian Ibarraran, Frank Hu et al. · 0 citations
Open access Aug 2026

Efficient, Few-Shot Directed Evolution with Energy Rank Alignment.

Directed evolution is a powerful and widely used technique for protein engineering, and reducing the cost of iterated experimental observations has become a major priority for practitioners. A number of recent efforts to use machine-learning-based predictors to improve sequence selection have led to remarkable improvem...

Sebastian Ibarraran, Shriram Chennakesavalu, Frank Hu et al. · 0 citations
#machine learning Preprint Sep 2026

Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling

Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for molecules that satisfy multiple, often competing, objectives. Large language models (LLMs) provide a useful generative prior for this problem because of their representational capacity, reasoning ability, and flexi...

Frank Hu, Shriram Chennakesavalu, Zi-Cheng Wang et al. · 0 citations

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