Core Viewpoint - The research introduces the XPert model, a dual-branch transformer designed to accurately predict drug-induced cellular perturbation responses, improving patient-specific response prediction accuracy by up to 15.04% while providing mechanistic interpretability [2][15]. Traditional Drug Development Challenges - Traditional drug development follows a "one drug - one target" model, but it is increasingly recognized that drugs interact with multiple molecular targets and pathways, leading to diverse phenotypic outcomes. Understanding genome-wide perturbation effects is crucial for elucidating drug mechanisms and optimizing treatments. However, the scarcity of high-quality perturbation data, especially in clinical settings, and confounding factors in perturbation data limit progress in this field [5]. Innovation of the XPert Model - The XPert model employs a dual-branch transformer architecture that encodes both pre- and post-perturbation cellular states, allowing it to distinguish intrinsic transcription patterns from regulatory changes triggered by perturbations. Each cell is represented as a gene-tagged "sentence" with a global cell state marker [7][8]. Performance of XPert - In benchmark tests, XPert consistently outperformed all baseline models, particularly excelling in challenging cold cell settings. In single-dose, single-time-point prediction tasks, XPert's Pearson correlation coefficient exceeded that of the next best model, TranSiGen, by 36.7%, with a mean squared error reduction of 78.2%. Even when faced with unseen cell lines during training, XPert demonstrated an average improvement of 67.54% over current state-of-the-art models, showcasing significant advancements in generalization capabilities [11][12]. Multi-Dose and Multi-Time Prediction - XPert supports multi-dose and multi-time predictions, accurately elucidating pharmacodynamic trajectories and revealing key molecular events behind drug effects. A case study using Vorinostat demonstrated that increasing doses typically enhanced gene impact, with PCA analysis confirming a clear dose-response gradient. Notably, changes in dose could reverse transcription effects, with XPert effectively capturing these subtle patterns [14]. Clinical Relevance and Insights - The research team explored the relationship between drug-induced transcriptomic changes and clinical responses. Analysis of patient data from Letrozole treatment revealed that responders exhibited stronger transcriptomic responses than non-responders. XPert uniquely identified additional key resistance biomarkers, such as TIAM1 and CDKN1B, which were "invisible" in expression level analyses, highlighting the potential of attention-based methods to uncover gene-phenotype associations and provide insights into resistance mechanisms [17]. Future Outlook - XPert represents a significant advancement in simulating drug-induced perturbation effects through an interpretable and generalizable deep learning framework. With further development, it is expected to become a core component of next-generation computer-aided drug discovery processes and precision medicine platforms [19][20].