Phenotype-based screening has attracted much attention for identifying
cell-active compounds. Transcriptional and proteomic profiles of cell
population or single cells are informative phenotypic measures of cellular
responses to perturbations. In this paper, we proposed a deep learning
framework based on encoder-decoder architecture that maps the initial cellular
states to a latent space, in which we assume the effects of drug perturbation
on cellular states follow linear additivity. Next, we introduced the cycle
consistency constraints to enforce that initial cellular state subjected to
drug perturbations would produce the perturbed cellular responses, and,
conversely, removal of drug perturbation from the perturbed cellular states
would restore the initial cellular states. The cycle consistency constraints
and linear modeling in latent space enable to learn interpretable and
transferable drug perturbation representations, so that our model can predict
cellular response to unseen drugs. We validated our model on three different
types of datasets, including bulk transcriptional responses, bulk proteomic
responses, and single-cell transcriptional responses to drug perturbations. The
experimental results show that our model achieves better performance than
existing state-of-the-art methods.

基于表型的筛选方法在识别具有活性的化合物方面引起了广泛的关注。本文提出了一种基于编码 - 解码架构的深度学习框架，可以将初始细胞状态映射到潜在空间中，其中假设药物对细胞状态的影响符合线性可加性。通过引入循环一致性约束条件，我们可以强制要求经过药物干扰的初始细胞状态产生受扰动的细胞响应，反之亦然，从受扰动的细胞状态中去除药物干扰则会恢复到初始细胞状态。循环一致性约束条件和潜在空间中的线性建模能够学习可解释且可迁移的药物干扰表示，从而使我们的模型能够预测对未知药物的细胞响应。我们在三种不同类型的数据集上验证了我们的模型，包括批量转录响应、批量蛋白响应以及单细胞转录响应对药物扰动。实验结果显示，我们的模型比现有的最先进方法取得了更好的性能。