Multi-Institution AI Security in Federated Drug Design Systems.

Abstract

Background: Drug discovery and development face critical data silos and collaboration barriers across pharmaceutical institutions, research centers, and healthcare organizations. While deep learning models demonstrate exceptional potential for accelerating drug discovery through pattern recognition in molecular data, genomic sequences, and clinical records, current approaches require centralized data aggregation that violates privacy regulations, compromises intellectual property, and limits cross-institutional collaboration essential for breakthrough therapeutic development.

Objective: This systematic review evaluates the effectiveness and implementation challenges of multi-institution AI security frameworks in federated drug design systems, examining how privacy-preserving collaborative learning can overcome traditional data sharing barriers while maintaining competitive advantages and regulatory compliance.

Methods: We conducted a comprehensive systematic review following PRISMA guidelines across PubMed, Scopus, IEEE Xplore, SpringerLink, Web of Science, and Google Scholar databases from 2015-2025. Search terms included "multi-institution AI security,""federated drug design systems,""privacy-preserving pharmaceutical AI," and "secure collaborative drug discovery." Articles were evaluated for technical security mechanisms, implementation effectiveness, scalability metrics, and practical deployment considerations. Data extraction focused on security performance quantification, privacy preservation guarantees, and real-world validation outcomes.

Results: Analysis of 105 selected studies reveals that multi-institutional federated learning frameworks achieve superior security effectiveness (92%) compared to traditional centralized approaches (72%), cloud-based hybrid systems (65%), and conventional distributed architectures (58%). Privacy-preserving federated learning with differential privacy (ε ≤ 0.1) and homomorphic encryption enables secure knowledge sharing across pharmaceutical institutions while preventing model inversion attacks and membership inference vulnerabilities. Industry-scale implementations, exemplified by the MELLODDY consortium involving ten pharmaceutical companies with 2.6+ billion confidential molecular data points, demonstrate successful cross-pharma collaboration without compromising proprietary information. Federated distillation approaches show 15-25% improvement in drug-target prediction accuracy while expanding applicability domains by up to 9.7% compared to single-institution models.

Conclusions: Multi-institution AI security frameworks represent a transformative paradigm for pharmaceutical research, enabling unprecedented collaboration while preserving competitive advantages and ensuring regulatory compliance with HIPAA, GDPR, and emerging AI governance requirements. The integration of differential privacy, secure multi-party computation, and federated distillation creates robust defense mechanisms against adversarial attacks while accelerating drug discovery timelines. These findings establish multi-institutional federated learning as an essential infrastructure for next-generation pharmaceutical research, offering a validated pathway to overcome data fragmentation barriers that currently limit therapeutic innovation and patient benefit.

Clinical Implications: Implementation of secure federated drug design systems could reduce drug development timelines by 20-30% through enhanced data diversity and improved model generalization, ultimately accelerating life-saving therapeutic delivery to patients while maintaining the highest standards of data protection and intellectual property security.

federated learning, multi-institutional collaboration, AI security, drug discovery, privacy-preserving machine learning, pharmaceutical research, differential privacy, secure computation, healthcare informatics

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