Experiments 2 and 3: Satisfying a Molecular Docking Constraint. In this section, we demonstrate that simple curricula, utilizing a single Curriculum Objective can accelerate agent productivity and generate compounds that satisfy a docking constraint, i.e., predicted to retain experimentally validated interactions (see Methods for experiment hyperparameters). Simulating a real-world application where one must allocate limited computational resources, baseline RL and CL performances are compared, given a maximum number of permitted production epochs (300), i.e., epochs that involve docking, as these are relatively computationally demanding. For CL, Curriculum Objectives are first applied to guide the agent and the number of permitted curriculum epochs is not limited, as these are computationally inexpensive (see Table ). Angiolini et al. design PDK1 inhibitors by leveraging the dihydro-pyrazoloquinazoline scaffold which forms two hydrogen-bonding interactions with Ala 162 (Fig. ) that are crucial for potency. The structure-based optimization is mimicked by defining the following Production Objective: Production Objective: Generate compounds that retain the two hydrogen-bonding interactions with Ala 162, possess enhanced predicted potency compared to the reference ligand (as assessed by LigPrep and Glide docking score) and are drug-like, as measured by the Quantitative Estimate of Druglikeness (QED). The QED score also prevents compounds from exploiting the weaknesses of docking algorithms to achieve favourable scores, e.g., presence of excessive hydrogen bond donors would significantly restrict membrane permeability. 31 First, we show that the Production Objective is challenging for baseline RL (Fig. and). The docking score is approximately 0 for the first 100 epochs, indicating essentially no compounds sampled satisfy the docking constraint. From epochs 100-200, some compounds satisfy the docking constraint but the score (averaged over all compounds sampled) is still low. It is only from epoch 200 onward that the docking score begins a steep improvement and indicates the point at which the agent starts entering a state of productivity. It is evident that baseline RL is suboptimal as the agent spends a significant amount of time generating compounds that do not satisfy the Production Objective. It is worth noting, however, that the agent eventually converges given enough time (Supplementary Fig. ).