MFAE: Multilevel Feature Aggregation Enhanced Drug‐Target Affinity Prediction for Drug Repurposing Against Colorectal Cancer

Abstract

Colorectal cancer (CRC), a leading cause of cancer‐related deaths globally, demands innovative therapeutic strategies to improve patient outcomes. Drug repurposing, identifying new uses for existing drugs, provides a cost‐effective solution. To this end, this study constructs the first drug‐target affinity dataset specifically for two novel therapeutic targets for CRC, P2X4 and mTOR, and designs a new deep learning‐based multilevel feature aggregation enhanced (MFAE) model. The model implements hierarchical feature extraction and multilevel feature aggregation and enhancement to simulate complex drug‐target interactions. Evaluations using fivefold cross‐validation on the collected CRC dataset showcase MFAE's superior predictive accuracy. Fine‐tuning of the model on external experimental data further enhances its performance, with a concordance index of 0.930, a determination coefficient of 0.782, and a mean squared error of 0.191. Ablation studies further highlight the key role of the group‐wise feature enhancement mechanism and ensemble learning strategy in enhancing the model's performance. Virtual screening of the Food and Drug Administration‐approved drugs identifies Ponatinib and Talazoparib as potential repurposing candidates. Despite limitations in experimental validation, this study establishes an innovative computational framework designed for CRC drug discovery. Overall, this research offers a valuable perspective on leveraging computational approaches for precision oncology.