Ensemble Siamese Neural Networks for Prodrug Activation Prediction

Prodrug design enhances critical drug properties without affecting potency by harnessing metabolic activation to release the active pharmaceutical ingredient (API). However, predicting prodrug activation remains challenging, causing design failures and limiting systematic exploration of the prodrug design space. Here, we developed an ensemble Siamese neural network model to predict prodrug activation. We demonstrate that our model accurately predicts 95% of all approved prodrugs, compared to less than 75% for any of the currently established metabolism models. Prospective validation on in-house designed prodrugs further confirmed the model’s ability to rank prodrug candidates by their observed release profiles. This work establishes a generalizable framework for prodrug activation prediction and provides a foundation for rational prodrug design, with the potential to accelerate the development of safer and more effective medicines.