most citedFaster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES

20 citations · 20 across the 1 of their papers we have counts for

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1 paper

physics.chem-ph2022★ 20 cited

Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES

Esben Jannik Bjerrum, Christian Margreitter, Thomas Blaschke +1

Using generative deep learning models and reinforcement learning together can effectively generate new molecules with desired properties. By employing a multi-objective scoring fun…