A Specialized and Enhanced Deep Generation Model for Active Molecular Design Targeting Kinases Guided by Affinity Prediction Models and Reinforcement Learning.

Abstract

Kinases are critical regulators in numerous cellular processes, and their dysregulation is linked to various diseases, including cancer. Thus, protein kinases have emerged as major drug targets at present, with approximately a quarter to a third of global drug development efforts focusing on kinases. Additionally, deep learning-based molecular generation methods have shown obvious advantages in exploring large chemical space and improving the efficiency of drug discovery. However, many current molecular generation models face challenges in considering specific targets and generating molecules with desired properties, such as target-related activity. Here, we developed a specialized and enhanced deep learning-based molecular generation framework named KinGen, which is specially designed for the efficient generation of small molecule kinase inhibitors. By integrating reinforcement learning, transfer learning, and a specialized reward module, KinGen leverages a binding affinity prediction model as part of its reward function, which allows it to accurately guide the generation process toward biologically relevant molecules with high target activity. This approach not only ensures that the generated molecules have desirable binding properties but also improves the efficiency of molecular optimization. The results demonstrate that KinGen can generate structurally valid, unique, and diverse molecules. The generated molecules exhibit binding affinities to the target that are comparable to known inhibitors, achieving an average docking score of -9.5 kcal/mol, which highlights the model's ability to design compounds with enhanced activity. These results suggest that KinGen has the potential to serve as an effective tool for accelerating kinase-targeted drug discovery efforts.