@inproceedings{3a6fdf30d4444a14840013a6c1725a3d,
title = "A PROPERTY-GUIDED DIFFUSION MODEL FOR GENERATING MOLECULAR GRAPHS",
abstract = "Inverse molecular generation is an essential task for drug discovery, and generative models offer a very promising avenue, especially when diffusion models are used. Despite their great success, existing methods are inherently limited by the lack of a semantic latent space that can not be navigated and perform targeted exploration to generate molecules with desired properties. Here, we present a property-guided diffusion model for generating desired molecules, which incorporates a sophisticated diffusion process capturing intricate interactions of nodes and edges within molecular graphs and leverages a time-dependent molecular property classifier to integrate desired properties into the diffusion sampling process. Furthermore, we extend our model to a multi-property-guided paradigm. Experimental results underscore the competitiveness of our approach in molecular generation, highlighting its superiority in generating desired molecules without the need for additional optimization steps.",
keywords = "Diffusion Model, Drug Discovery, Molecular Graph Generation",
author = "Changsheng Ma and Taicheng Guo and Qiang Yang and Xiuying Chen and Xin Gao and Shangsong Liang and Nitesh Chawla and Xiangliang Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSP48485.2024.10447350",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2365--2369",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
address = "United States",
}