Fig. 7: The framework of MMCLKin.

a Geometric graph network module models the local and global spatial interactions of kinases and drugs using 3D kinases, 3D binding pockets, and 3D molecules. b Sequence network module leverages large language models and BiLSTMs to extract evolutionary information from kinase and pocket sequences, alongside chemical features from SMILES. c A multi-head attention mechanism is applied to further identify dependency relationships across varying ranges within kinase-drug interaction systems operating at diverse modalities and scales, while quantifying the contribution of each component to the prediction task. d Prediction module is used to generate the predictive results based on the concatenated interaction features from various modalities and scales. e Multimodal and multiscale contrastive learning with attention consistency (MMCLAC) method aligns attention coefficients across different modalities and scales for elements within the same domain, ensuring the model effectively captures kinase-drug interaction features from diverse perspectives while distinguishing binding differences among diverse interaction systems. \({{\mathbb{R}}}_{13{kd}},{{\mathbb{R}}}_{13{pd}},{{\mathbb{R}}}_{1{kpd}}\) and \({{\mathbb{R}}}_{3{kpd}}\) denote the shared domains of four paired interactions (1D and 3D kinase-drug interactions, 1D and 3D pocket-drug interactions, 1D kinase-drug and 1D pocket-drug interactions, 3D kinase-drug and 3D pocket-drug interactions) used for contrastive learning, respectively, and \({{\mathbb{P}}}_{{kd}-1d}^{13{kd}},{{\mathbb{P}}}_{{kd}-3d}^{13{kd}},{{\mathbb{P}}}_{{pd}-1d}^{13{pd}},{{\mathbb{P}}}_{{kd}-3d}^{13{kd}},{{{\mathbb{P}}}_{{kd}-1d}^{1{kpd}},{{\mathbb{P}}}_{{pd}-1d}^{1{kpd}}{\mathbb{,}}{\mathbb{P}}}_{{kd}-3d}^{3{kpd}},{{\mathbb{P}}}_{{pd}-3d}^{3{kpd}}\) represent the corresponding relative attention probability sets within the defined shared domains.