Saeed Izadi

This study evaluates PS-SINS for predicting high-concentration viscosity of IgG1 antibodies, showing strong correlation with viscosity and improved model accuracy when combined with in silico data and DLS.

This paper develops a machine learning model to predict Trp oxidation risk in antibodies and shows that modulating electrostatic potential reduces oxidation while maintaining binding affinity.

This paper predicts antibody clearance using deep learning and physicochemical features, showing comparable performance to physicochemical models.

The paper introduces MolDesk, molecular surface descriptors for predicting antibody developability, and shows they outperform existing models in predicting clinical progression.