May 8, 2024, 4:42 a.m. | Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.03987v1 Announce Type: new
Abstract: Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules. However, beyond generating new random molecules, efficient exploration and a comprehensive understanding of the vast chemical space are of great importance to molecular science and applications in drug design and materials discovery. In this paper, we propose a new framework, ChemFlow, to traverse chemical space through navigating the latent space learned …

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