Interpretably Predicting Chemical Perturbation via Biologically Informed Visible Neural Network

Recent advances in single-cell transcriptomics have enabled high-resolution characterization of cellular responses to drug perturbations, a critical capability for precision medicine and drug discovery. Deep learning has emerged as a powerful tool for accurate and scalable prediction of single-cell responses to drug perturbations. However, most existing approaches fail to consider gene-level structural priors and biological knowledge. Furthermore, they often operate as black boxes with limited interpretability. To offer reliable drug response prediction in real-world applications, there is an urgent need to develop a model that combines high predictive accuracy with strong interpretability. In this work, we propose a novel framework based on the Visible Neural Network (VNN) to predict cellular responses to drug perturbations in single-cell transcriptomic profiles. In contrast to traditional black-box models, VNN incorporates prior biological knowledge into its architecture. This integration enables interpretable predictions while maintaining biological coherence. We further integrate deep learning models with a knowledge graph of gene-gene relationships to improve the understanding and generalization of transcriptomic responses to unseen drug perturbations by leveraging prior biological knowledge of gene interactions. Our analysis reveals that the hierarchical structure of the VNN aligns well with known biological processes and supports robust feature attribution. In general, our findings underscore the potential of visible architectures to bridge deep learning and systems biology for a scalable and interpretable modeling of cellular drug responses.