A Reinforcement Learning-Based Reward Mechanism for Molecule Generation that Introduces Activity Information

In this paper, we propose an activity prediction method for molecule generation based on the framework of reinforcement learning. The method is used as a scoring module for the molecule generation process. By introducing information about known active molecules for specific set of target conformations, it overcomes the traditional molecular optimization strategy where the method only uses computable properties. Eventually, our prediction method improves the quality of the generated molecules. The prediction method utilized fusion features that consist of traditional countable properties of molecules such as atomic number and the binding property of the molecule to the target. Furthermore, this paper designs a ultra large-scale molecular docking parallel computing method, which greatly improves the performance of the molecular docking [1] scoring process. The computing method makes the high-quality docking computing to predict molecular activity possible. The final experimental result shows that the molecule generation model using the prediction method can produce nearly twenty percent active molecules, which shows that the method proposed in this paper can effectively improve the performance of molecule generation.