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


In the context of pharmaceutical research, privacy-preserving decentralized approaches are crucial since they would allow for the enrichment of chemical and biological space for predictive modeling. When applied to collaborative activities, traditional and central ML techniques exhibited shortcomings.

Aljoša Smajić, Melanie Grandits, Gerhard F. Ecker, Privacy-preserving techniques for decentralized and secure machine learning in drug discovery, Drug Discovery Today, Volume 28, Issue 12, 2023 



Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed for ML model-building tasks. Some examples of the different techniques are secure multiparty computation, distributed deep learning, homomorphic encryption, blockchain-based peer-to-peer networking, differential privacy, and federated learning, as well as combinations of such techniques. In this paper, we present an overview of these techniques for decentralized ML to illustrate its benefits and drawbacks in the field of drug discovery.


The Pharmacoinformatics Research Group (Ecker lab) acknowledges funding provided by the Austrian Science Fund FWF AW012321 MolTag.

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Figure 3. Simplified depiction of a fully homomorphic encryption.

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