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.

本文提出了一种用于临床前药物筛选中药物反应预测的零样本学习方法，称为MSDA。MSDA可以与传统的药物反应预测方法无缝集成，通过学习类似药物的先前反应数据的不变特征，增强对未标记化合物的实时预测性能。研究结果表明，MSDA能够有效地预测未知化合物的药物反应，使临床前药物筛选阶段的总体性能提高了5-10％。该解决方案的重要性在于能够加速药物发现过程，改善药物候选物评估并促进药物发现的成功。

药物预测的零样本学习方法用于临床前药物筛选