Recent advances have focused in identifying molecules with desirable properties in large chemical spaces. To this end, genetic algorithms (GAs) , methods using variational autoencoders (VAEs) , recurrent neural networks (RNNs) , and generative antagonistic networks (GANs) have been developed. In several instances, these algorithms are associated with the generation of new molecules and have exhibited the ability to traverse chemical space more effectively, reaching optimal chemical solutions while considering fewer molecules than allowed by the brute-force screening of large chemical libraries. Similarly, several evolutionary and RNN selection mechanisms have proven successful in multi-objective optimization problems . Emerging approaches in chemical enumeration incorporate chemical reactions into ML-based generation to design novel compounds in a synthetically accessible chemical space .