Predicting patient-specific drug responses from preclinical cell-line data remains challenging due to significant heterogeneity between preclinical (cell-line) and clinical (patient) gene expression profiles. In this study, we propose WANCDR, a novel adversarial neural network framework designed to improve the generalization of drug-response predictions by aligning latent representations across preclinical and clinical domains. Specifically, we introduce a domain alignment module trained adversarially, which enforces the encoder to generate domain-invariant latent embeddings. Extensive experiments conducted on preclinical (GDSC) and clinical (TCGA) datasets demonstrate that WANCDR achieves robust predictive performance on preclinical data, while substantially outperforming existing approaches in clinical generalization, particularly when classifying responses for previously unseen drugs. Qualitative analyses via UMAP visualization further validate the superior domain alignment capability of WANCDR. Collectively, these results highlight the potential of WANCDR to bridge the translational gap from preclinical insights to clinical applications.