The former approaches perform discrete space optimization often utilizing Reinforcement Learning (RL). Such methods can be applied directly on the molecular graph to identify actions, i.e., structural modifications such as inserting or deleting atoms or bonds, that will improve the property of interest , . The challenge here is that structural modifications may violate chemical rules resulting in invalid molecular structures and therefore accounting for chemical knowledge through chemical rules or heuristics is deemed necessary . An alternate approach is to operate at a substructure level combining molecular fragments to compose a molecular graph , . Finally, sequence-based approaches have also been explored employing RL methods for controlling SMILES generation. Such approaches are built upon language models which offer the advantage of being pre-trained on vast chemical datasets and therefore capturing well the chemical language and validity , .