PREpiBind — research checkpoints (float32)

Four MHC class II peptide-binding models, one per measurement type, in the precision they were trained in. Weights only: the optimizer state and the epoch counter are dropped, nothing else is changed.

Code and everything needed to run these: https://github.com/daylight-00/PREpiBind
float16 copies of the same four models, for the notebooks and Colab: daylight-00/prepibind-demo
The backbone they call: daylight-00/esmc-300m-2024-12
The full-length HLA embedding store they read: daylight-00/prepibind-embeddings

The four files

file	arm	seed	fold	val_loss	predict config
`prepibind_qualitative_s100_f0.pt`	qualitative binding assays	100	0	0.35582	`configs/predict/config_demo.py`
`prepibind_ms_s128_f3.pt`	mass-spectrometry eluted ligands	128	3	0.12274	`configs/predict/config_ms.py`
`prepibind_ic50_500_s128_f2.pt`	IC50, binder threshold < 500 nM	128	2	0.46631	`configs/predict/config_ic50_500.py`
`prepibind_ic50_1000_s42_f1.pt`	IC50, binder threshold < 1000 nM	42	1	0.49456	`configs/predict/config_ic50_1000.py`

Each is 213.0 MiB, a one-key {"model_state_dict": ...} dict of 64 float32 tensors, 55,820,161 parameters. md5, in table order: e0d105b1…, fd30ab20…, c589a329…, 708a3753….

All four are the val_loss argmin of their arm.

How these four were chosen, and why they are not the paper's numbers

Each arm was trained as 15 runs: three seeds (42, 100, 128) by five cross-validation folds, and every number reported in the paper is a mean over those 15. A released checkpoint is one of the fifteen — the one with the lowest validation loss, the value in the table above. No test-set quantity took part in the choice. For a benchmark comparison, use the paper's means, not one checkpoint.

Quote the validation losses from analysis/val_metrics.csv in the repository; the val_loss column of analysis/scoring/*/per_run_results.csv is joined in from that file and carries the same numbers. The val_auc column in either is not usable: it comes from a roc_auc_score call with its arguments swapped.

Held-out test sets, never used for selection: 48,352 rows (qualitative), 33,490 (ms), 14,150 (both IC50 arms).

Training data

From the IEDB Export v3, split into arms by measurement type. The rebuild is pipeline/preprocess/ in the repository; the resulting CSVs are data/dataset/ there.

arm	directory	train rows	test rows	target column	positives (train)
qualitative	`data/dataset/full/`	112,871	48,352	`Target`	58.1 %
ms	`data/dataset/ms_ql/`	77,954	33,490	`Target`	39.4 %
ic50_500	`data/dataset/ic50/`	33,004	14,150	`Target_500`	36.7 %
ic50_1000	`data/dataset/ic50/`	33,004	14,150	`Target_1000`	46.0 %

Training: AdamW, lr 1e-5, betas (0.9, 0.999), eps 1e-8, weight decay 0.01, five-fold CV, best epoch by validation loss (20, 27, 28, 28 for the four files above — the epoch each source checkpoint recorded, and the corrected argmin of its run). Both sides were fed from precomputed ESMC 300M embeddings, emb_hla_esmc_small_0430.h5 for the HLA chains and the matching epitope store.

Architecture

prepibind.model.plm_cat_mean_inf(hla_dim=960, epi_dim=960, head_div=64), 55,820,161 parameters:

2 self-attention blocks over the HLA chain-pair embeddings, 2 over the epitope embeddings, 15 heads each (960 // 64)
1 joint self-attention block over the two concatenated token sequences
masked mean pool, then Linear(960, 480) -> GELU -> Dropout -> Linear(480, 1), one logit

Inputs are per-residue ESMC 300M embeddings, 960-dimensional. The head is not a language model and never sees a raw sequence: the epitope is encoded at run time by ESMC 300M, the HLA alpha and beta chains are read from a precomputed store.

Input format

Two columns. The MHC field is the two chain names joined by an underscore. Both names must be keys in the HLA mapping table the config points at, spelled exactly as that table spells them. The datasets and the demo write beta first, then alpha; the order does not change the score, because the head carries no positional encoding and pools over tokens, but following the convention keeps inputs comparable with the published ones.

MHC,Epitope
HLA-DRB1*01:01_HLA-DRA*01:01,PKYVKQNTLKLATA
HLA-DPB1*03:01_HLA-DPA1*01:03,AAAARYPNVTIAAAA
H2-IAbB_H2-IAbA,AAAAPAAAATTAAPA

Output is the input with Logits and Score (sigmoid of the logit) appended.

Two limits worth knowing before feeding it anything:

Every training and test epitope is a 15-mer. The arms were normalised to 15 residues. Other lengths run, since the encoder pads and masks, but they are outside the training distribution.
Coverage is 116 chains: 98 human HLA class II chains and 18 mouse H2 chains, the ones the four arms actually use. The research embedding store carries 134 keys; the other 18 are further human alleles curated alongside them, and no released model was trained to score them.

Usage

git clone https://github.com/daylight-00/PREpiBind && cd PREpiBind
pip install --extra-index-url https://download.pytorch.org/whl/cu126 -e ".[demo]"

hf download daylight-00/prepibind           prepibind_qualitative_s100_f0.pt --local-dir models
hf download daylight-00/esmc-300m-2024-12   esmc_300m_2024_12_v0_fp16.pth    --local-dir models
hf download daylight-00/prepibind-embeddings emb_hla_esmc_small_0430.h5 --repo-type dataset --local-dir emb

The ESMC encoder is vendored into the package (prepibind/esmc/) and runs on torch alone, so inference needs no pip install esm.

from prepibind.inference import load_config, main

cfg = load_config(
    "configs/predict/config_demo.py",                        # the qualitative model's config
    chkp_path="models/prepibind_qualitative_s100_f0.pt",     # this float32 checkpoint
    hla_emb_path="emb/emb_hla_esmc_small_0430.h5",           # full-length store
    hla_path="data/mhc_mapping/HLA2_IMGT_MSA_idx_edit.csv",  # mapping WITH the windows
    test_path="my_input.csv",
    out_path="outputs",
)
cfg["Test"]["precision"] = "as-trained"   # bfloat16 ESMC, float32 head. The default in that
                                          # config is "fp16", which is the demo's setting.
df = main(cfg)                            # writes outputs/prediction.csv, returns the DataFrame

Three things have to agree, and the code will tell you if they do not: a full-length store needs the mapping table that carries the window as sequence|start|end (data/mhc_mapping/HLA2_IMGT_MSA_idx_edit.csv), while the bundled demo store is already cut to the window and needs demo/data/mhc_mapping_demo.csv, which has none. Applying a window twice raises.

The CLI does the same thing for the configs' own settings:

python -m prepibind.inference configs/predict/config_ms.py --chkp_path models/prepibind_ms_s128_f3.pt --plot

No GPU is required, and the runtime says whatever it had to drop: without CUDA, float32 throughout and no flash-attn; on a pre-Ampere card, flash-attn off and the bfloat16 ESMC demoted to float16, head unchanged.

Limitations and known caveats

One run, not the reported mean. See above. For a benchmark comparison, use the 15-run means in the paper, not one checkpoint.
Class II only. No class I allele is in the training data or in the embedding store.
The embedding store moved after training. emb_hla_esmc_small_0430.h5 as published in daylight-00/prepibind-embeddings received the 2026-09-08 H2 chain-swap correction and differs from what these checkpoints were trained on in 3 of its 134 keys: H2-IAdA and H2-IAdB, which held each other's arrays, and H2-IAg7A, same shape and different values. The correction is disclosed, not retro-applied. All three are mouse H2 chains and all three are in the training data — 0.48 % to 0.66 % of the rows of each arm — so predictions for H2-IAd and H2-IAg7 come from a model fit on the pre-fix arrays. Every human HLA embedding is unchanged. The dataset card has the per-arm counts.
Training is not bit-reproducible across GPU models, so a retrained checkpoint will differ in the last decimals. Inference on fixed inputs is deterministic.
The models score binding, not immunogenicity. A high score is not a prediction that a peptide is presented in vivo, still less that it elicits a response.

Licence

MIT for these weights and for the PREpiBind code. The backbone is not ours but is also MIT: ESMC 300M is now biohub/esmc-300m-2024-12, and esm 3.4.0 — the release the vendored prepibind/esmc/ source comes from — ships a plain MIT licence. Training data derives from IEDB (CC BY 4.0) and allele sequences from IPD-IMGT/HLA (CC BY-NoDerivs) and UniProt (CC BY 4.0). The repository's THIRD_PARTY_NOTICES.md has the detail.

Citation

@article{jang2026prepibind,
  title   = {PREpiBind: Protein Representation-integrated Epitope-MHC Class II Binding Prediction},
  author  = {Jang, David Hyunyoo and Kim, Dongwoo and Hwang, Untaek and Park, Byungho and Choi, Yoonjoo and Lee, Juyong},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.09.15.751749},
  url     = {https://www.biorxiv.org/content/10.64898/2026.09.15.751749v1}
}

Posted on bioRxiv 2026-09-21 as doi:10.64898/2026.09.15.751749, CC BY 4.0.

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