Drug development is a protracted and expensive process. One of the main challenges indrug discovery is to find molecules with desirable properties. Molecular optimization is thetask of optimizing precursor molecules by affording them with desirable properties. Recentadvancement in Artificial Intelligence, has led to deep learning models designed for molecularoptimization. These models, that generates new molecules with desirable properties, have thepotential to accelerate the drug discovery. In this thesis, I evaluate the current state-of-the-art graph-to-graph translation model formolecular optimization, the HierG2G. I examine the HierG2G’s performance using three testcases, where the second test is designed, with the help of chemical experts, to represent a commonmolecular optimization task. The third test case, tests the HierG2G’s performance on,for the model, previously unseen molecules. I conclude that, in each of the test cases, the HierG2Gcan successfully generate structurally similar molecules with desirable properties givena source molecule and an user-specified desired property change. Further, I benchmark the HierG2Gagainst two famous string-based models, the seq2seq and the Transformer. My resultsuggests that the seq2seq is the overall best model for molecular optimization, but due to thevarying performance among the models, I encourage a potential user to simultaneously use allthree models for molecular optimization.