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Fig. 3 | Journal of Cheminformatics

Fig. 3

From: DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

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 (\( r_{t} \))

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