Experiment 1: Target Scaffold Construction. As an initial example, we show that CL can guide the agent to generate compounds possessing a relatively complex scaffold that is not present in the training set for the prior (Fig. ). The dihydro-pyrazoloquinazoline scaffold was identified as a promising starting point for PDK1 inhibitor design owing to good cell permeability and low promiscuity and initial SAR analysis kept the phenyl substituent constant. The goal is to generate compounds that possess this target scaffold. We first demonstrate that the task is too complex for standard policy-based RL and denote this as baseline RL (Supplementary Fig. ). In the baseline experiment, the only component in the scoring function is target scaffold. Each generated compound scores either 1.0 or 0.5, denoting whether the scaffold is present or not, respectively. The average score of the baseline experiment does not exceed 0.5 across 2000 epochs, indicating the scaffold is not found. Given that the scaffold is not present in the training set, the likelihood of sampling a compound possessing the scaffold is much lower and the inability to do so prevents meaningful agent learning. It is worth noting that provided unlimited time, baseline RL will almost surely find the scaffold due to sampling stochasticity. On the other hand, CL can accelerate convergence by decomposing the target scaffold into simpler substructures with gradually increasing structural complexity (Fig. ). There are 5 Curriculum Objectives, each assigned to successively more complex substructures with Curriculum Progression Criterion thresholds of 0.8. The agent is tasked to generate compounds possessing each substructure until the average score is 0.8. When a Curriculum Progression Criterion is satisfied, the successive and more complex Curriculum Objective is activated. A sharp decrease in average score accompanies each Curriculum Objective update, e.g., at approximately epoch 150 (Fig. ), as it is unlikely that currently sampled compounds will also possess the successive substructure, by chance. Over the course of training, the agent learns to generate compounds possessing increasingly complex substructures until the target scaffold is constructed.