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Optimization of Molecules via Deep Reinforcement Learning
We present a framework, which we call Molecule Deep Q-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and randomized value functions). We directly define modifications on molecules, ther...
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| Publicado no: | Sci Rep |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6656766/ https://ncbi.nlm.nih.gov/pubmed/31341196 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-019-47148-x |
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