Author

Natasa Tagasovska

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#artificial intelligence Preprint Oct 2026

PairIT: Autoregressive Transformers for Low-Data Molecule Optimization

PairIT is introduced, a flexible training framework for autoregressive transformers over SMILES sequences that enables seeded generation by learning from pairs of structurally similar molecules, and Prompt tokens extend this framework by specifying the target property, desired direction, and approximate magnitude of ch...

Hannah Lawrence, Bodhi P. Vani, Ji Won Park et al. · 0 citations

#protein folding Open access Aug 2026

DyAb: sequence-based antibody design and property prediction in a low-data regime

DyAb is a pair-wise representation built on top of a pre-trained protein language model that achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across monoclonal antibodies targeting three different antigens.

J. Lin, Jennifer L. Hofmann, A. Leaver-Fay et al. · mAbs · 0 citations