Drug-side effect frequency prediction using an asymmetric multi-task learning approach

Abstract Identifying the frequencies between drugs and side effects is crucial in drug development. As clinically validating potential side effects is financially expensive and time consuming, computational methods offer an appealing alternative for predicting candidate side effects. Since this prediction problem is inherently complex, many methods treated the problem as two tasks: association identification and frequency estimation. These tasks differ greatly in their objectives and learning dynamics, as association demands discrete classification, while frequency requires continuous regression. The previous multi-task based methods employ symmetric architectures that fail to address this divergence. To tackle this issue, we propose a novel method that employs a novel dual-task approach with asymmetric model architecture with a dedicated sub-network for each task. Our empirical study demonstrates that the proposed method achieves the best performance compared with the state-of-the-art approaches achieving 15.6% and 2.8% improvement in area under the precision–recall curve for classification, and achieving 2.2% and 31.8% reduction in mean absolute error for regression compared to the second-best method under warm-start and cold-start settings, respectively. Further experiments suggest there is a performance gain in using an asymmetric architecture than symmetric architectures.