Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design

The design of small molecules with bespoke properties is of central importance to drug discovery. However significant challenges yet remain for computational methods, despite recent advances such as deep recurrent networks and reinforcement learning strategies for sequence generation, and it can be difficult to compare results across different works... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
A2C
Policy Gradient Methods
PPO
Policy Gradient Methods