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MGDM: Molecular generation using a multinomial diffusion model
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DOI：10.1016/j.ymeth.2025.03.001.png)
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Abstract
En 中文
Accurate analysis of molecular structures and the rapid generation of valid molecules remain significant challenges in De Novo drug design. In this study, we propose the Multinomial Generated Diffusion Model (MGDM) for molecular generation. This model leverages a multinomial diffusion framework to process discrete data, with a focus on learning the multinomial distribution inherent in the dataset. During the generation process, the model progressively denoises molecules, transitioning from a uniform noise distribution to ultimately produce valid molecular structures. Initially, we generate molecules unconditionally to expand the compound library. In the next phase, we focus on generating molecules with specific properties to assess the model's capacity for conditional generation. For this, we implement a classifier-free guidance strategy, which directs the diffusion model's task without the need for training separate classifier models. To validate the effectiveness of our framework, we conducted experiments using the Molecular Sets (MOSES) dataset. The results demonstrate that, compared to several state-of-the-art methods, MGDM generates valid molecules while achieving superior or comparable performance in terms of novelty and diversity.
Keywords:
Molecular generation
Multinomial diffusion
Denoise
Gumbel-Max sampling
Kullback-Leibler divergence