With the model encoder being formulated as q θ (z|x) and the model decoder being p θ (x|z) and P (x) being the distribution of the training dataset and P(z) the latent space prior. Optimization of molecules takes place in the latent space of an encoder-decoder model through a RL paradigm. An overview of the MOLRL methodology is presented in Figure . An RL agent interacts with the environment (the latent space) through a Markov decision process (MDP) defined by a set of states (s t ∈ S), a set of actions (a t ∈ A), rewards (R t ∈ R) and transition probabilities P (s t+1 |s t , a t ). The states correspond to points in the latent space, the actions determine how a latent vector will be modified and finally the rewards are determined by a (task-specific) predefined reward function which evaluates the outcome of each action.