DMPKformer: An Interpretable Multimodal Deep Learning Framework for Reliable ADMET Property Prediction (opens in new tab)

Accurate prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a critical challenge in drug discovery. Traditional single modality approaches often fail to capture the complex, multi-scale relationships governing molecular behaviour across physicochemical, structural, and pharmacokinetic dimensions. In this work, we propose a multi-modal deep learning framework that integrates complementary molecular representations, MACCS fingerprints, molecul...