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Privacy-preserving Data Science on QSAR models

This case study shows the effectiveness of Apheris’ platform and services for data science on data that is not directly accessible and distributed. Federated learning decentralizes learning algorithms to access multiple data sets, maintaining privacy. Without pooling sensitive data, new insights can be uncovered securely.

In this white paper, you will learn:

checkmark How federated learning, privacy testing and other tools that enable data science on not directly accessible and distributed data
checkmark Sowcase of federated learning, privacy testing and other tools that enable data science practices on not directly accessible and distributed data, at the example of QSAR models.

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In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

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