  Accurate and robust drug response prediction is of utmost importance in
precision medicine. Although many models have been developed to utilize the
representations of drugs and cancer cell lines for predicting cancer drug
responses (CDR), their performances can be improved by addressing issues such
as insufficient data modality, suboptimal fusion algorithms, and poor
generalizability for novel drugs or cell lines. We introduce TransCDR, which
uses transfer learning to learn drug representations and fuses multi-modality
features of drugs and cell lines by a self-attention mechanism, to predict the
IC50 values or sensitive states of drugs on cell lines. We are the first to
systematically evaluate the generalization of the CDR prediction model to novel
(i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR
shows better generalizability than 8 state-of-the-art models. TransCDR
outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP)
from scratch under various scenarios. The most critical contributors among
multiple drug notations and omics profiles are Extended Connectivity
Fingerprint and genetic mutation. Additionally, the attention-based fusion
module further enhances the predictive performance of TransCDR. TransCDR,
trained on the GDSC dataset, demonstrates strong predictive performance on the
external testing set CCLE. It is also utilized to predict missing CDRs on GDSC.
Moreover, we investigate the biological mechanisms underlying drug response by
classifying 7,675 patients from TCGA into drug-sensitive or drug-resistant
groups, followed by a Gene Set Enrichment Analysis. TransCDR emerges as a
potent tool with significant potential in drug response prediction. The source
code and data can be accessed at https://github.com/XiaoqiongXia/TransCDR.
