{"ID":57750,"CreatedAt":"2026-02-27T13:00:40Z","UpdatedAt":"2026-02-27T13:00:40Z","DeletedAt":null,"paper_url":"https://paperswithcode.com/paper/optimization-of-molecules-via-deep","arxiv_id":"1810.08678","title":"Optimization of Molecules via Deep Reinforcement Learning","abstract":"We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN),\nfor molecule optimization by combining domain knowledge of chemistry and\nstate-of-the-art reinforcement learning techniques (double $Q$-learning and\nrandomized value functions). We directly define modifications on molecules,\nthereby ensuring 100\\% chemical validity. Further, we operate without\npre-training on any dataset to avoid possible bias from the choice of that set.\nInspired by problems faced during medicinal chemistry lead optimization, we\nextend our model with multi-objective reinforcement learning, which maximizes\ndrug-likeness while maintaining similarity to the original molecule. We further\nshow the path through chemical space to achieve optimization for a molecule to\nunderstand how the model works.","short_abstract":"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).","url_abs":"http://arxiv.org/abs/1810.08678v3","url_pdf":"http://arxiv.org/pdf/1810.08678v3.pdf","authors":"[\"Zhenpeng Zhou\", \"Steven Kearnes\", \"Li Li\", \"Richard N. Zare\", \"Patrick Riley\"]","published":"2018-10-19T00:00:00Z","tasks":"[\"Deep Reinforcement Learning\", \"Molecular Graph Generation\", \"Multi-Objective Reinforcement Learning\", \"Q-Learning\", \"reinforcement-learning\", \"Reinforcement Learning\", \"Reinforcement Learning (RL)\"]","methods":"[]","has_code":false,"code_links":[{"ID":760,"CreatedAt":"2026-02-27T13:01:31Z","UpdatedAt":"2026-02-27T13:01:31Z","DeletedAt":null,"paper_id":57750,"paper_url":"https://paperswithcode.com/paper/optimization-of-molecules-via-deep","paper_title":"Optimization of Molecules via Deep Reinforcement 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