Fig. 3.

The reinforcement learning pathway for systemic generation of molecules (Redrawn from You et al. [34]). a The state is defined as the current graph $G_t$ and the possible atom types $C$. b The GCPN conducts message passing to encode the state as node embeddings and estimates the policy function. c The action to be performed ($a_t$) is sampled from the policy function. The environment performs a chemical valency check on the intermediate state and returns (d) the next state $G_t$ and (e) the associated reward ($r_t$)