A Review on AI-Driven Prediction and Visualization of Kinase Inhibitor Binding Landscapes

The development of kinase inhibitors has revolutionized targeted therapy specifically in cancer therapy by blocking aberrant cell signaling pathways. Despite the therapeutic potential, traditional drug discovery for kinase inhibitors is confronted with many challenges including but not limited to the high cost of drug development, lengthy process for drug discovery, and poor specificity due to off-target effects. But with the rise of Artificial Intelligence (AI) came new tools that can help transcend these limitations, promising an alternative route towards accelerating the discovery of kinase inhibitors and optimizing therapeutic strategies. AI-based techniques such as Convolutional Neural Networks and advanced models, including AlphaFold2 (for protein structure prediction) have opened new avenues for investigating kinase-inhibitor binding interactions that are the target for further development of multi-target therapies with minimal side effects. In this context, this comprehensive review summarizes the novel approaches used in AI-empowered prediction and visualization of the binding landscapes of kinase inhibitors, with a strong emphasis on the utilization of convolutional neural networks, AlphaFold2, and molecular dynamics. This review also provides real and practical directions for researchers and practitioners in computational drug design and molecular biology to optimize kinase inhibitor development and therapeutic strategies using deep learning techniques such as Transformer based models for molecular sequences, Protein Interaction Graph Network go under the acronym PIGNet, and Contact Prediction for Ligand-Protein Complexes abbreviated as ConPLex. In the end, it outlines future directions and possible solutions to the challenges faced by AI-mediated drug discovery. This review article will potentially become the reference for scientists and practitioners interested in the application of AI methodologies for kinase inhibitor.