WELLCOME TO IPI ML based developability prediction.

IPIAbDev is a highly flexible AI-ML Framework, designed for high-throughput prediction of antibody biophysical properties, with a primary focus on polyreactivity (PSR) and SEC developability, while being extensible to additional liabilities. It integrates multiple antibody-specific protein language models (AbLang2, AntiBERTy, AntiBERTa2, AntiBERTa2-CSSP) for embedding generation and supports a diverse set of classifiers, including XGBoost, Random Forest, 1D-CNN with residual blocks, and Transformer architectures. Key features include automated embedding generation, HCDR3-cluster-stratified k-fold cross-validation to prevent data leakage, model training and prediction for binary classification tasks, and built-in interpretability via Integrated Gradients for residue-level attribution. The package also provides publication-ready visualization of ROC curves, performance metrics (AUC, accuracy, F1, precision, recall), and attribution heatmaps

Download and install: IPIAbDev package

git clone https://github.com/proteininnovation/IPIAbdev.git

#Create a new environment with Python 3.11 or 3.12 conda create -n ml python=3.11 -y conda activate ml

#python package install conda install -c bioconda anarci pip install -r requirements.txt

How to use IPIAbDev

prepare train set

filename: your_trainset_name.xlsx required columns: BARCODE,CDR3,HSEQ,LSEQ, any_biophysical_properties_column as sec_filter,psr_filter ,spr_filter any_biophysical_properties_column : this label should be annotated as 1 (pass/positive) or 0 (fail or negative)

Generate embeddings

python predict_developability.py --build-embedding data/test.xlsx --lm all

python predict_developability.py --build-embedding data/test.xlsx --lm ablang

Xgboost trainning evaluation with k-fold validation

python predict_developability.py --kfold 10 --target sec_filter --lm antiberta2 --model xgboost --db data/ipi_antibody.xlsx

Randomforest training evaluation with k-fold validation

python predict_developability.py --kfold 10 --target sec_filter --lm antiberty --model rf --db data/ipi_antibody.xlsx

CNN trainning evaluation with k-fold validation

python predict_developability.py --kfold 10 --target sec_filter --lm antiberta2 --model cnn --db data/ipi_antibody.xlsx

Transformer trainning evaluation with k-fold validation

python predict_developability.py --kfold 10 --target sec_filter --lm antiberty --model transformer_lm --db data/ipi_antibody.xlsx

Transformer with one hot encoding trainning evaluation with k-fold validation

python predict_developability.py --kfold 10 --target sec_filter --lm onehot --model transformer_onehot --db data/ipi_antibody.xlsx

Train final model with full dataset

#SEC trainning

python predict_developability.py --train --target sec_filter --lm antiberta2 --model xgboost --db data/ipi_antibody.xlsx

#PSR trainning

python predict_developability.py --train --target sec_filter --lm antiberta2 --model xgboost --db data/ipi_antibody.xlsx

Predict instantly

python predict_developability.py --predict data/new_lib.xlsx --target sec_filter --lm antiberta2

Predict on test set

python predict_developability.py --predict data/test.xlsx --target sec_filter --lm ablang --model cnn

Contact: {Hoan.Nguyen, Andre.Teixeira}@proteininnovation.org