The goal of drug discovery is to design novel, non-patented molecules with desired molecular and therapeutic properties. Traditionally, medicinal chemists have relied on their own chemical intuition to design novel small molecules drugs. However, one of the difficulties associated with such a process is that multiple criteria, such as safety, bioavailability, etc., must be simultaneously satisfied in order to be a successful drug. Because many criteria must be concurrently met, the search for a new drug against a given target is an example of a classic multi-objective optimization problem. Due to dramatic improvements in GPU hardware and the predictive power of machine learning and deep learning methods, there has been a growth in interest to apply these state-of-the-art techniques to facilitate the generation of new molecules. My work focuses on combining the atomic-based and functional group-based modifications via synthetic organic reactions through reinforcement learning to enable compound predictions that are synthetically accessible while at the same time addresses the classic multi-objective optimization problem in drug discovery.