This work developed HCAbLM, to its knowledge the first foundation model pretrained specifically on a large-scale fully human HCAb repertoire, and its frozen representations transferred to experimentally measured SEC purity, HIC behavior and thermal stability in grouped internal validation and retrospective cross-project evaluation.
OmniTCR bridges heterogeneous immune sequence data, providing a foundation for computational immunology and receptor design, and achieved the highest sequence recovery on internal and external generation benchmarks.
Fei-Ran Zeng, Duanyu Feng, Dan-Dan Song et al.· bioRxiv· 0 citations
GenCDR, a family of LLaMa-based autoregressive language models that read all frameworks as a conditioning prompt and generate all CDRs jointly as a variable-length response, makes CDR likelihoods a clean, separable target for reward attribution, achieves the highest CDR recovery among autoregressive models.
Ferran Gonzalez Hernandez, Oliver M. Turnbull, Maliha Sultana et al.· bioRxiv· 0 citations
Antibodies exhibit species-specific sequence and structural features that influence their antigen-recognition properties. Although several studies have investigated porcine antibodies, their repertoire and structural characteristics remain less well characterized than those of several other mammalian species. In this s...
The most recent methods substantially outperformed earlier ones, producing medium-or-better top-ranked models for approximately half of post-cutoff Fv complexes without templates or experimental restraints, and performing similarly on antigens with or without a close pre-cutoff homolog.
Minjae Park, Roman Nett, Brian M. Petersen et al.· bioRxiv· 0 citations
Although protein toxins represent valuable pharmacological templates, predicting toxicity directly from primary sequences is inherently challenging because of their evolutionary dynamics. Active toxins and their benign homologues frequently share identical structural scaffolds and differ by only a few key residue subst...
Seongmin Kim, Min-Seok Kim, Chungoo Park· bioRxiv· 0 citations
HelixDTA is introduced, a deep learning framework featuring a parallel dual-branch architecture that independently learns representations from two distinct but complementary modalities: sequence context and complete target structures that demonstrates significant potential to empower precision drug design and accelerat...
Shang Lou, Xu-Hua Li, Yu-Jie Peng et al.· Journal of Chemical Informat...· 0 citations
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