Privacy-preserving techniques for decentralized and secure machine learning in drug discovery

Area of Science:

• Computational chemistry and cheminformatics
• Artificial intelligence in medicine and pharmacology

Background:

• Data availability, security, and privacy are critical barriers to machine learning (ML) efficiency.
• Sensitive data in drug discovery necessitates specialized ML approaches for model development.

Purpose of the Study:

• To provide an overview of decentralized ML techniques applicable to drug discovery.
• To illustrate the benefits and drawbacks of these novel methods in the pharmaceutical field.

Main Methods:

• Exploration of secure multiparty computation.
• Review of distributed deep learning frameworks.
• Analysis of homomorphic encryption, blockchain, differential privacy, and federated learning.
• Examination of hybrid approaches combining multiple privacy-preserving techniques.

Main Results:

• Decentralized ML offers viable solutions for leveraging sensitive data in drug discovery.
• Each technique presents unique advantages and limitations regarding privacy, security, and computational overhead.
• Combinations of methods can potentially enhance overall performance and security.

Conclusions:

• Decentralized ML techniques are crucial for advancing drug discovery while safeguarding data.
• Careful consideration of technique-specific benefits and drawbacks is essential for optimal implementation.
• Further research into hybrid models promises to unlock greater potential in privacy-preserving ML for pharmaceuticals.