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Accurate and robust drug response prediction is of utmost importance inprecision medicine. Although many models have been developed to utilize therepresentations of drugs and cancer cell lines for predicting cancer drugresponses (CDR), their performances can be improved by addressing issues suchas insufficient data modality, suboptimal fusion algorithms, and poorgeneralizability for novel drugs or cell lines. We introduce TransCDR, whichuses transfer learning to learn drug representations and fuses multi-modalityfeatures of drugs and cell lines by a self-attention mechanism, to predict theIC50 values or sensitive states of drugs on cell lines. We are the first tosystematically evaluate the generalization of the CDR prediction model to novel(i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDRshows better generalizability than 8 state-of-the-art models. TransCDRoutperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP)from scratch under various scenarios. The most critical contributors amongmultiple drug notations and omics profiles are Extended ConnectivityFingerprint and genetic mutation. Additionally, the attention-based fusionmodule further enhances the predictive performance of TransCDR. TransCDR,trained on the GDSC dataset, demonstrates strong predictive performance on theexternal testing set CCLE. It is also utilized to predict missing CDRs on GDSC.Moreover, we investigate the biological mechanisms underlying drug response byclassifying 7,675 patients from TCGA into drug-sensitive or drug-resistantgroups, followed by a Gene Set Enrichment Analysis. TransCDR emerges as apotent tool with significant potential in drug response prediction. The sourcecode and data can be accessed at https://github.com/XiaoqiongXia/TransCDR.

TransCDR: a deep learning model for enhancing the generalizability of  cancer drug response prediction through transfer learning and multimodal data  fusion for drug representation