Multi-party collaborative drug discovery via federated learning

In the field of drug discovery and pharmacology research, precise and rapid prediction of drug-target binding affinity (DTA) and drug-drug interaction (DDI) are essential for drug efficacy and safety. However, pharmacological data are often distributed across different institutions. Moreover, due to concerns regarding data privacy and intellectual property, the sharing of pharmacological data is often restricted. It is difficult for institutions to achieve the desired performance by solely utilizing their data. This urgent challenge calls for a solution that not only enhances collaboration between multiple institutions to improve prediction accuracy but also safeguards data privacy. In this study, we propose a novel federated learning (FL) framework to advance the prediction of DTA and DDI, namely FL-DTA and FL-DDI. The proposed framework enables multiple institutions to collaboratively train a predictive model without the need to share their local data. Moreover, to ensure data privacy, we employ secure multi-party computation (MPC) during the federated learning model aggregation phase. We evaluated the proposed method on two DTA and one DDI benchmark datasets and compared them with centralized learning and local learning. The experimental results indicate that the proposed method performs closely to centralized learning, and significantly outperforms local learning. Moreover, the proposed framework ensures data security while promoting collaboration among institutions, thereby accelerating the drug discovery process.