Zero-shot Learning of Drug Response Prediction for Preclinical Drug
  Screening
Kun Li Yong Luo Xiantao Cai Wenbin Hu Bo Du
Conventional deep learning methods typically employ supervised learning for
drug response prediction (DRP). This entails dependence on labeled response
data from drugs for model training. However, practical applications in the
preclinical drug screening phase demand that DRP models predict responses for
novel compounds, often with unknown drug responses. This presents a challenge,
rendering supervised deep learning methods unsuitable for such scenarios. In
this paper, we propose a zero-shot learning solution for the DRP task in
preclinical drug screening. Specifically, we propose a Multi-branch
Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can
be seamlessly integrated with conventional DRP methods, learning invariant
features from the prior response data of similar drugs to enhance real-time
predictions of unlabeled compounds. We conducted experiments using the GDSCv2
and CellMiner datasets. The results demonstrate that MSDA efficiently predicts
drug responses for novel compounds, leading to a general performance
improvement of 5-10\% in the preclinical drug screening phase. The significance
of this solution resides in its potential to accelerate the drug discovery
process, improve drug candidate assessment, and facilitate the success of drug
discovery.