ChemistryCommunications Chemistry
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity
T. Ochiai, T. Inukai, et al.
Discover the groundbreaking NP-VAE, a novel deep-learning approach that excels in managing large molecular structures, particularly natural compounds with chirality. This innovative method not only achieves remarkable reconstruction accuracy but also enables the generation of novel compounds with enhanced functions. Research conducted by Toshiki Ochiai, Tensei Inukai, Manato Akiyama, Kairi Furui, Masahito Ohue, Nobuaki Matsumori, Shinsuke Inuki, Motonari Uesugi, Toshiaki Sunazuka, Kazuya Kikuchi, Hideaki Kakeya, and Yasubumi Sakakibara.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Chemistry
Representation of molecular structures with persistent homology for machine learning applications in chemistry
J. Townsend, C. P. Micucci, et al.
Engineering and Technology
Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing
B. Alimkhanuly, J. Sohn, et al.
Chemistry
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
M. Moret, I. P. Angona, et al.
Chemistry
A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
H. Park, X. Yan, et al.
