Known Neighbor Generated GSK3𝛽 = 0.78 JNK3 = 0.63 GSK3𝛽 = 0.75 JNK3 = 0.56 Following the evaluation strategy by Jin et al. , we record the top-5000 highest reward molecules generated during the optimization process and compute the following metrics: i) success rate ii) novelty, and iii) diversity. Success rate is computed as the percentage of molecules that have succeeded in the top-5000. A generated molecule is deemed successful if the predicted probabilities for binding towards each target are greater or equal to 0.5, and the following cutoffs for the QED and SA scores are satisfied: QED ≥ 0.6 and SA ≤ 4. Novelty examines the closeness of the generated molecules to the active molecules used for training the predictive classification models for activity to GSK3β and JNK3. In particular, it is computed as the fraction of molecules with nearest neighbor similarity, with respect to the train set, lower than 0.4 among the succeeded ones. Finally, diversity examines how similar or dissimilar structurally the succeeded molecules are. In particular it is computed by subtracting the average pairwise similarities from 1 with higher values indicating lower average similarity and therefore higher diversity. The molecular similarity is calculated as the Tanimoto similarity on Morgan fingerprints. Success Novelty Diversity JT-VAE 1.3% --GCPN 4.0% --REINVENT 47.9% 56.1% 0.621 RationaleRL 74.8% 56.8% 0.701 FaST Tables 4 shows the performance of MOLRL trained in the latent space of the VAE-CYC and MOLRL trained in the MolMIM space as well as the performances of state-of-the-art molecule optimization methods as reported in the literature .