Lei Deng

This paper introduces a self-supervised learning method, ATMOL, for molecular property prediction using attention-wise graph masking and contrastive learning, achieving state-of-the-art performance.

DeepDDS uses a graph neural network with attention to predict synergistic drug combinations, outperforming existing methods on benchmark and independent test data.

This paper introduces pathDNN, a pathway-guided deep neural network that improves interpretability and predictive performance in drug sensitivity modeling for cancer cells.

DrugCombDB integrates drug combination data with quantitative dose-response information to support experimental and computational screening of drug combinations.