The algorithm is extremely modular. This study is a proof-of-concept of its capabilities. Many components in the problem of computer-aided drug design (CADD) can be independently optimized. The algorithm provides scope to allow necessary changes to be incorporated in light of important future developments. The reinforcement model we use is a simple Graph Convolution Network. However, more complex models, such as Attention networks , could be used for greater success with enough training time and a potentially better-formulated reward function.