Beyond the ability to generate valid and unique de novo molecules adhering to a pre-defined scaffold, it is further of interest to be able to condition molecule generation according to some desirable endpoint, for example, predicted binding affinity or synthesizability. LibINVENT is a encoder-decoder style architecture that translates an input scaffold into output decorations. Furthermore, the training dataset is curated by splitting molecules based on a set of reaction rules, therefore, encouraging the implicit learning of reaction rules and improving synthesizability of proposed de novo molecules. We re-implemented the first experiment conducted by the authors of LibINVENT. Firstly, we pre-train an RNN based on the same hyperparameters and training dataset provided by the authors. Note that we only take unique complete (non-sliced) molecules from the training dataset for training. Then we re-implement the RL objective tasks in MolScore using the same trained QSAR model to predict the probability of Dopamine Receptor D2 (D 2 ) activity and a re-implementation of the reaction filters. The RL algorithm used is the same as REINVENT and is likewise compared to LibINVENT over a run for 100 epochs.