  The aim of the inverse chemical design is to develop new molecules with given
optimized molecular properties or objectives. Recently, generative deep
learning (DL) networks are considered as the state-of-the-art in inverse
chemical design and have achieved early success in generating molecular
structures with desired properties in the pharmaceutical and material chemistry
fields. However, satisfying a large number (larger than 10 objectives) of
molecular objectives is a limitation of current generative models. To improve
the model's ability to handle a large number of molecule design objectives, we
developed a Reinforcement Learning (RL) based generative framework to optimize
chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune
the pre-trained generative network allowed the model to satisfy up to 21
objectives and increase the generative network's robustness. The experiments
show that the proposed multiple-objective RL-based generative model can
correctly identify unknown molecules with an 83 to 100 percent success rate,
compared to the baseline approach of 0 percent. Additionally, this proposed
generative model is not limited to just chemistry research challenges; we
anticipate that problems that utilize RL with multiple-objectives will benefit
from this framework.
