arXiv:2603. 10302v3 Announce Type: replace Abstract: A plethora of protein language models have been released in recent years.
arXiv:2607. 16263v1 Announce Type: new Abstract: Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data.
arXiv:2603. 13431v3 Announce Type: replace-cross Abstract: Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development.
arXiv:2607. 20057v1 Announce Type: cross Abstract: Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules.
arXiv:2606. 02386v1 Announce Type: new Abstract: Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical feedback or redirect generation when a candidate violates thermodynamic or structural constraints.
arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.
Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules. Although recent protein language models have enabled progress in single-chain protein modeling and generation, they often fall short in antigen-specific antibody design, where effective modeling requires explicit pairing between antibody and antigen, particularly at the epitope level.
Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery. However, existing methods treat affinity comparisons independently and ignore the contextual information encoded in other labeled comparisons, limiting their ability to capture antigen-specific binding landscapes.
arXiv:2605. 21610v2 Announce Type: replace Abstract: Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input.
arXiv:2607. 05846v1 Announce Type: cross Abstract: Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery.
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
arXiv:2607. 18835v1 Announce Type: cross Abstract: Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface.
arXiv:2608. 02684v1 Announce Type: cross Abstract: Large Language Models (LLMs) are accelerating biological research, yet this same capability poses a critical biosecurity threat: models that assist in protein engineering can equally be prompted to generate predicted toxin-like sequences, potentially lowering the barrier to biological misuse.