Fig. 2
From: Memory-assisted reinforcement learning for diverse molecular de novo design
Schematic comparison of regular and memory-assisted reinforcement learning utilizing a QSAR model. a The activity prediction surface of a non-linear QSAR model is illustrated. A generative model iteratively constructs compounds (green stars), which are predicted to be active. b Using regular reinforcement learning, the model generates only compounds of the first local maximum it reaches. c Memory-assisted reinforcement learning starts with regular reinforcement learning. d Once the chemical space is locally explored, the memory alters the prediction surface and forces the generative model to find a new local maximum