Abstract <p>Purpose: Predicting drug–target interactions (DTIs) is a practical demand in drug development and drug repositioning. Therefore, developing accurate and efficient DTI prediction methods has significant application value. Current models focus on the features of either drugs or targets independently, and concatenate them together for downstream prediction. They ignore the hidden associations between drugs and targets, which may affect the implementation of DTIs. Methods: In this work, we design a contrastive learning model to fuse intramolecular and intermolecular features of drugs and targets, named IIC-DTI. The intramolecular features focus on drug chemical structures and target amino acid sequences, which are generated separately. Meanwhile, the intermolecular features are focused on drug–target pairs, extracted by a multi-head cross-attention network. For the two embeddings of either a drug or a target in two views, a contrastive learning module is applied to update the embedding of one view by fusing information from the other view. Those novel embeddings are concatenated and fed into a 3-hidden layer neural network for predicting potential DTIs. Results: Multiple comparative experiments show that our proposed model has better performance than nine state-of-the-art methods, including two pre-trained large language models, according to several evaluation metrics on four benchmark datasets. In case study, 16 out of 20 drug–target pairs were verified by literature evidence. Moreover, IIC-DTI identified related interactions of a given drug and target successfully. It indicates that IIC-DTI has the potential application to identify DTIs in realistic conditions.</p> Graphical Abstract <p></p>