Cardiotoxicity (CT) involving diminished cardiac contractility and heart failure is a major adverse event associated with otherwise efficacious protein kinase inhibitors (KIs). Here, we sought to develop clinically-weighted transcriptomic signatures to predict risk of CT and to better understand the biological processes associated with CT risk. We obtained transcriptome-wide response profiles in four human primary cardiomyocyte cell lines that were treated with 22 different KIs using mRNA sequencing with 3' digital gene expression. The FDA Adverse Event Reporting System was used to derive relative risk scores for four types of CT for different KIs. We used elastic net regression to associate these transcriptomic profiles with KI-associated risk scores for CT subtypes to obtain clinically-weighted transcriptomic signatures, which showed good predictive properties (cross-validation R2 >0.87). Our clinically-weighted transcriptomic signatures for KI-associated CT may be of relevance in early drug development for the prediction of KI-associated CT.