Typical objectives guiding the drug discovery process are maximisation of predicted efficiencies, synthetic accessibility or drug-likeness of the compounds, and minimising off-target effects and toxicity. Even without optimisation towards favourable physicochemical and pharmacokinetic properties, DNDD is inherently a multi-objective optimisation (MOO) problem. In this application note, we present the new open-source software library DrugEx, a tool for de novo design of small molecules with deep learning generative models in a multi-objective reinforcement learning (RL) framework. This comprehensive tool represents the consolidation of the original work of Liu et al.'s multiple scripts based DrugEx releases. The first version of DrugEx 6 consisted of a recurrent neural network (RNN) single-task agent of gated recurrent units (GRU) which were updated to long short-term memory (LSTM) units in the second version, 7 also introducing MOO-based RL and an updated exploitation-exploration strategy. In its third version, generators based on a variant of the transformer and a novel graph-based encoding allowing for the sampling of molecules with specific substructures were introduced. These developments were built on the work of for the use of reinforcement learning, that of Arus-Pous et al. and of Yang et al.'s SynthaLinker 13 for the recurrent neural network and transformer architectures respectively. Here all these versions and capabilities have been consolidated in a single package.