{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimization-of-molecules-via-deep","title":"Optimization of Molecules via Deep Reinforcement Learning","arxiv_id":"1810.08678","date":"2018-10-19","proceeding":null,"authors":["Zhenpeng Zhou","Steven Kearnes","Li Li","Richard N. Zare","Patrick Riley"],"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.","url_abs":"http://arxiv.org/abs/1810.08678v3","url_pdf":"http://arxiv.org/pdf/1810.08678v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/google-research/google-research/tree/master/mol_dqn","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/caiyingchun/MolDQN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/danilonumeroso/MEG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/junyoung0131/Mol-DQN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/tangxiangru/RL-for-RNA-design","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_11_optimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"optimization-of-molecules-via-deep","repo_url":"https://github.com/aksub99/MolDQN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"molecular-graph-generation","task_name":"Molecular Graph Generation"},{"task_slug":"multi-objective-reinforcement-learning","task_name":"Multi-Objective Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.08678","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}