  Pharmaceutical industry can better leverage its data assets to virtualize
drug discovery through a collaborative machine learning platform. On the other
hand, there are non-negligible risks stemming from the unintended leakage of
participants' training data, hence, it is essential for such a platform to be
secure and privacy-preserving. This paper describes a privacy risk assessment
for collaborative modeling in the preclinical phase of drug discovery to
accelerate the selection of promising drug candidates. After a short taxonomy
of state-of-the-art inference attacks we adopt and customize several to the
underlying scenario. Finally we describe and experiments with a handful of
relevant privacy protection techniques to mitigate such attacks.
