DELPHI: Pre-trained antibody developability models (PSR and SEC)
Authors/Creators
- 1. Institute For Protein Innovation
Description
This repository accompanies the DELPHI (Deep End-to-end Learning Platform
for antibody developability with High Interpretability) platform from the
Institute for Protein Innovation (IPI).
DELPHI platform: https://github.com/proteininnovation/delphi
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
PRETRAINED MODELS (trained on IPI internal datasets)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Models trained on proprietary IPI antibody developability datasets.
Training sequences cannot be shared due to confidentiality.
PSR filter models (polyreactivity):
FINAL_psr_filter_*.pt / .pkl
SEC filter models (size exclusion chromatography):
FINAL_sec_filter_*.pt / .pkl
Supported architectures: TransformerOneHot, TransformerLM
(ABlang2, AntiBERTy, AntiBERTa2, IgBERT), Random Forest, XGBoost
Usage:
python delphi.py --list-models
python delphi.py --predict my_antibodies.xlsx \
--model_id FINAL_psr_filter_onehot_transformer_onehot_ipi_psr.pt
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
DS1 — PRE-PROCESSED PUBLIC DATASET
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Pre-processed version of the human antibody polyreactivity dataset
from Chen et al. 2024 (Cell Reports, PMC11564698, MIT License).
Processing applied to the original dataset:
- HCDR3 extracted via ANARCI (IMGT numbering, positions 105-117)
- Columns standardised to DELPHI format:
BARCODE — unique antibody identifier
HSEQ — VH amino acid sequence
LSEQ — VL amino acid sequence
CDR3 — HCDR3 sequence (ANARCI IMGT 105-117)
psr_filter — 1=PASS (low polyreactivity), 0=FAIL (high)
DS1.xlsx — full dataset (246,293 antibodies)
DS1.xlsx.ablang.emb.csv — ABlang2 embeddings (480-dim)
DS1.xlsx.antiberta2.emb.csv — AntiBERTa2 embeddings (1024-dim)
DS1.xlsx.antiberta2-cssp.emb.csv — AntiBERTa2-CSSP embeddings (1024-dim)
DS1.xlsx.antiberty.emb.csv — AntiBERTy embeddings (512-dim)
DS1.xlsx.igbert.emb.csv — IgBERT embeddings (1024-dim)
Source: Chen HT, Zhang Y, Huang J et al. Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions.Cell Reports, 2024.
10.1016/j.celrep.2024.114801
Original data: https://zenodo.org/records/14735846 (MIT License)
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Additional details
Identifiers
Dates
- Created
-
2026-06
Software
- Programming language
- Python