All Competitors

Pan-allele peptide-MHC binding prediction unifying MHC class I and II, trained on a diversity-balanced set of 214 class I and 98 class II alleles.

Antibody-specific epitope prediction that replaces sequence-offset rotary attention with backbone local-frame 3D geometry. 0.410 MCC on AsEP.

Structure-based conformational B-cell epitope predictor that scores local antigen surface patches with ESM-2 embeddings and an ensemble MLP.

Protein deimmunization framework that aligns a protein language model with GRPO to design sequences carrying fewer MHC Class I T-cell epitopes.

Linear B-cell epitope prediction from peptide sequence alone, pairing ProtT5 embeddings with an SVM trained on 222,030 curated IEDB peptides.

Generative protein language model that designs synthetic linear epitope libraries, and classifiers that filter them by bacterial or viral origin.

B-cell epitope predictor pairing CNN and Transformer branches over protein language model embeddings to score linear and conformational epitopes.

B-cell epitope predictor fusing ESM-2 embeddings with residue contact and protrusion features to score linear and conformational epitopes.

Allele-free HLA class I epitope classification from peptide sequence alone, via LoRA-adapted ESM-2 with parallel CNN and Transformer branches.

Linear B-cell epitope prediction for cancer antigens, pairing ESM-2 embeddings with an MLP classifier; ROC-AUC 0.94 on a held-out IEDB benchmark.

T-cell epitope immunogenicity prediction that fuses MHC presentation, TCR binding, and activation data via adversarial multi-domain pretraining.

Linear B-cell epitope prediction using phylogeny-aware fine-tuning of ESM embedders, with taxon-specific models for 19 pathogen groups.

Antibody-aware B-cell epitope prediction from a graph convolutional network over frozen antibody and antigen protein language model embeddings.

Epitope prediction model scoring whether a peptide is presented by HLA class I or II, with no allele input needed. Built on ESM-2 embeddings.

Generative transformer that writes candidate cognate epitope sequences from a TCR CDR3-beta input, annotating repertoires without functional assays.

HLA class II presentation and CD4 epitope prediction from peptide sequence, built on a protein language model with learned allele deconvolution.

Contrastive dual-encoder aligning T-cell receptor CDR3 and peptide epitope sequences in one latent space to rank which receptors bind which antigens.

Peptide-MHC presentation model for class I and II that maps peptide, flanking residues, source protein and MHC allele into separate readable vectors.