DC Field | Value | Language |
---|---|---|
dc.contributor.author | AHN, SUNGSOO | - |
dc.contributor.author | Kim, Junsu | - |
dc.contributor.author | Lee, Hankook | - |
dc.contributor.author | Shin, Jinwoo | - |
dc.date.accessioned | 2022-02-25T05:40:43Z | - |
dc.date.available | 2022-02-25T05:40:43Z | - |
dc.date.created | 2022-02-25 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/109513 | - |
dc.description.abstract | De novo molecular design attempts to search over the chemical space for molecules with the desired property. Recently, deep learning has gained considerable attention as a promising approach to solve the problem. In this paper, we propose genetic expert-guided learning (GEGL), a simple yet novel framework for training a deep neural network (DNN) to generate highly-rewarding molecules. Our main idea is to design a “genetic expert improvement” procedure, which generates high-quality targets for imitation learning of the DNN. Extensive experiments show that GEGL significantly improves over state-of-the-art methods. For example, GEGL manages to solve the penalized octanol-water partition coefficient optimization with a score of 31.40, while the best-known score in the literature is 27.22. Besides, for the GuacaMol benchmark with 20 tasks, our method achieves the highest score for 19 tasks, in comparison with state-of-the-art methods, and newly obtains the perfect score for three tasks. Our training code is available at https://github.com/sungsoo-ahn/genetic-expert-guided-learning. | - |
dc.language | English | - |
dc.publisher | Neural information processing systems foundation | - |
dc.relation.isPartOf | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.relation.isPartOf | Advances in Neural Information Processing Systems | - |
dc.title | Guiding deep molecular optimization with genetic exploration | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.citation.conferenceDate | 2020-12-06 | - |
dc.citation.conferencePlace | US | - |
dc.citation.title | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.contributor.affiliatedAuthor | AHN, SUNGSOO | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
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