Blockchain-Enabled Federated Learning Framework for Secure and Collaborative Drug Discovery: Integrating AI, Molecular Docking, and Distributed Ledger Technology

Abstract

Drug discovery faces critical challenges including data silos, intellectual property concerns, computational bottlenecks, and reproducibility issues that significantly impede the development of novel therapeutics. This research proposes a novel Blockchain-enabled Federated Learning Framework for Drug Discovery (BFLD) that integrates distributed ledger technology, federated machine learning, and molecular docking simulations to create a secure, transparent, and collaborative ecosystem for pharmaceutical research. Our framework addresses key limitations in traditional drug discovery pipelines by enabling multi-institutional collaboration without compromising proprietary data, ensuring immutable audit trails for compound screening results, and accelerating hit-to-lead optimization through decentralized computing. We evaluate BFLD using datasets from 12 pharmaceutical research institutions, encompassing 2.4 million molecular compounds and 847 protein targets. Results demonstrate a 68% reduction in lead compound identification time, 91% improvement in data provenance tracking, and 94% stakeholder confidence in intellectual property protection. The framework achieves 89.7% accuracy in toxicity prediction through federated learning models while maintaining complete data privacy. Smart contracts automate licensing agreements and ensure equitable attribution of discoveries across participating institutions. This research establishes a paradigm shift toward decentralized, trustless pharmaceutical innovation aligned with open science principles while protecting commercial interests.