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Machine learning driven acceleration of biopharmaceutical formulation development using Excipient Prediction Software (ExPreSo)

First ML Tool for Excipient Prediction
The study introduces ExPreSo, the first machine-learning algorithm designed to predict stabilizing excipients for biopharmaceutical formulations.

Built on 335 FDA-Approved Formulations
ExPreSo was trained on one of the largest curated datasets of commercially approved protein/peptide drug formulations, covering diverse drug modalities including mAbs and non-mAbs.
Strong Predictive Accuracy for 9 Common Excipients
The model accurately predicts the presence of nine widely used excipients (e.g., histidine, polysorbate 80, sucrose), showing ROC-AUC values >0.7 for most in cross-validation tests.
Resilient Across Model Variants
Whether using interpretable features, fast models, or protein-only input data, ExPreSo consistently showed similar predictive performance with minimal overfitting.
Accelerates Formulation Development
ExPreSo supports early formulation decision-making by reducing experimental workload, enabling smarter excipient pre-selection, and lowering risks.

First ML Tool for Excipient Prediction
The study introduces ExPreSo, the first machine-learning algorithm designed to predict stabilizing excipients for biopharmaceutical formulations.

Built on 335 FDA-Approved Formulations
ExPreSo was trained on one of the largest curated datasets of commercially approved protein/peptide drug formulations, covering diverse drug modalities including mAbs and non-mAbs.
Strong Predictive Accuracy for 9 Common Excipients
The model accurately predicts the presence of nine widely used excipients (e.g., histidine, polysorbate 80, sucrose), showing ROC-AUC values >0.7 for most in cross-validation tests.
Resilient Across Model Variants
Whether using interpretable features, fast models, or protein-only input data, ExPreSo consistently showed similar predictive performance with minimal overfitting.
Accelerates Formulation Development
ExPreSo supports early formulation decision-making by reducing experimental workload, enabling smarter excipient pre-selection, and lowering risks.

First ML Tool for Excipient Prediction
The study introduces ExPreSo, the first machine-learning algorithm designed to predict stabilizing excipients for biopharmaceutical formulations.

Built on 335 FDA-Approved Formulations
ExPreSo was trained on one of the largest curated datasets of commercially approved protein/peptide drug formulations, covering diverse drug modalities including mAbs and non-mAbs.
Strong Predictive Accuracy for 9 Common Excipients
The model accurately predicts the presence of nine widely used excipients (e.g., histidine, polysorbate 80, sucrose), showing ROC-AUC values >0.7 for most in cross-validation tests.
Resilient Across Model Variants
Whether using interpretable features, fast models, or protein-only input data, ExPreSo consistently showed similar predictive performance with minimal overfitting.
Accelerates Formulation Development
ExPreSo supports early formulation decision-making by reducing experimental workload, enabling smarter excipient pre-selection, and lowering risks.

First ML Tool for Excipient Prediction
The study introduces ExPreSo, the first machine-learning algorithm designed to predict stabilizing excipients for biopharmaceutical formulations.

Built on 335 FDA-Approved Formulations
ExPreSo was trained on one of the largest curated datasets of commercially approved protein/peptide drug formulations, covering diverse drug modalities including mAbs and non-mAbs.
Strong Predictive Accuracy for 9 Common Excipients
The model accurately predicts the presence of nine widely used excipients (e.g., histidine, polysorbate 80, sucrose), showing ROC-AUC values >0.7 for most in cross-validation tests.
Resilient Across Model Variants
Whether using interpretable features, fast models, or protein-only input data, ExPreSo consistently showed similar predictive performance with minimal overfitting.
Accelerates Formulation Development
ExPreSo supports early formulation decision-making by reducing experimental workload, enabling smarter excipient pre-selection, and lowering risks and costs in formulation screening.