ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction

Deep learning has made significant progress in drug-target interaction (DTI) prediction. However, most existing approaches are developed using fixed datasets, which limits their applicability and generalizability due to the restricted scale of current datasets. This leads to significant discrepancies across the vast chemical space, particularly affecting model performance for previously unseen drugs or targets. In this study, we introduce ASCENT, an active transfer learning framework for DTI prediction. ASCENT utilizes an adaptive active learning strategy to expand datasets by dynamically selecting and annotating the most representative and uncertain samples based on model performance. To improve transferability, an entropy-based adversarial method was incorporated to align feature spaces between source and target domains during training. These innovations enable ASCENT to efficiently capture patterns across vast chemical spaces, thereby enhancing predictive accuracy while reducing annotation costs. Experimental results validate ASCENT's superiority in cross-domain applications, demonstrating its capacity to rapidly and effectively explore chemical diversity. Notably, ASCENT achieves desired performance levels while decreasing annotation expenditures by approximately 20%. Additionally, four representative case studies underscore ASCENT's potential in novel drug discovery and drug repurposing. These results highlight ASCENT as a valuable methodological advancement that supports accelerated drug development and provides new perspectives on DTI prediction.