Evaluation metrics. Following previous works, we evaluate our generative model on three target metrics, success, novelty and diversity. 5,000 molecules are generated by the model, and the metric scores are computed as follows: Success rate (SR) measures the proportion of generated molecules that fit the desired properties. For inhibition of GSK3β and JNK3, this is a score of at least 0.5 from the pretrained predictor. QED has a target score of ≥ .6 and SA has a target score of ≤ 4. Novelty (Nov) measures how different the generated molecules are compared to the set of actives in the dataset, and is the proportion of molecules whose Morgan fingerprint Tanimoto similarity is at most 0.4 to any molecule in the active set (range [0, 1]). Diversity (Div) measures how different the generated molecules are compared to each other, computed as an average of pairwise Morgan fingerprint Tanimoto similarity across all generated compounds (range [0, 1]). PM is the product of the three metrics above (PM = SR • Nov • Div). Implementation details. We construct the initial set of molecules for our search algorithm from the rationales extracted from . These rationales are obtained through a sampling process, Monte Carlo Tree Search (MCTS), on the active molecules that tries to minimize the size of the rationale subgraph, while maintaining their inhibitory properties. Rationales for multi-property tasks (GSK3β+JNK3) are extracted by combining the rationales for single-property tasks. Initializing generation with subgraphs is commonly done in molecular generative models such as Shi et al. Baseline methods. Rationale-RL extracts rationales of the active molecules and then uses RL to train a completion model that add atoms to the rationale in a sequential manner to generate molecules satisfying the desired properties. GA+D & JANUS are two genetic algorithms that use random mutations of SELFIES strings to generate promising molecular candidates; JANUS leverages a two-pronged approach, accounting for mutations towards both exploration and exploitation. MARS (Xie et al., 2021) uses Markov Chain Monte Carlo (MCMC) sampling to iterative build new molecules by adding or removing fragments, and the model is trained to fit the distribution of the active molecules. To provide a fair comparison against baselines that do not use rationales, we additionally include a baseline MARS+Rationale that initialize the MARS algorithm with the same starting initial rationale set used in Rationale-RL and our method. where possible, we use the numbers from the original corresponding paper.