Sorry, you need to enable JavaScript to visit this website.

DE NOVO MOLECULE GENERATION WITH GRAPH LATENT DIFFUSION MODEL

DOI:
10.60864/qx45-kr37
Citation Author(s):
Conghao Wang, Hiok Hian Ong, Shunsuke Chiba, Jagath C. Rajapakse
Submitted by:
Conghao Wang
Last updated:
6 June 2024 - 10:21am
Document Type:
Presentation Slides
Document Year:
2024
Event:
Presenters:
Yihao Chan
Paper Code:
BISP-L8.2
Categories:
 

De novo generation of molecules is a crucial task in drug discovery. The blossom of deep learning-based generative models, especially diffusion models, has brought forth promising advancements in de novo drug design by finding optimal molecules in a directed manner. However, due to the complexity of chemical space, existing approaches can only generate extremely small molecules. In this study, we propose a Graph Latent Diffusion Model (GLDM) that operates a diffusion model in the latent space modeled by a pretrained autoencoder. Applying diffusion processs on latent representations rather than original molecular graphs, GLDM improves training efficiency and enables generation of larger drug-like molecules. GLDM achieves state-of-the-art results on GuacaMol benchmarks.

up
0 users have voted: