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- W4386257480 abstract "Molecule conformer generation is an important task in several scientific fields, such as bioinformatics, pharmacology, and material discovery, to name a few, that aims to construct the 3D structure of a molecule. Many properties of a molecule are determined by its 3D structure. Normally, we can do experiments to determine a range of structures of a molecule. However, conformers are not available in certain circumstances, partly because of limited resources. As a result, alternate techniques for building 3D buildings are critical. In drug discovery, conformers of the molecule can be generated by computational means. In the past decades, a large number of conformer generation approaches have been developed for molecules. These procedures, however, are time-consuming and produce a large number of conformers. Recently, machine learning has come into play as a computational tool for accelerating the process of conformer generation with high-quality samples. This paper shows our research on a diffusion model for generating conformers." @default.
- W4386257480 created "2023-08-30" @default.
- W4386257480 creator A5056421988 @default.
- W4386257480 date "2023-08-09" @default.
- W4386257480 modified "2023-09-28" @default.
- W4386257480 title "Score-based Diffusion Model for Conformer Generation" @default.
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- W4386257480 doi "https://doi.org/10.1109/icit58056.2023.10225872" @default.
- W4386257480 hasPublicationYear "2023" @default.
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