In data-driven drug discovery, designing molecular descriptors is a very important task. Deep generative models such as variational autoencoders (VAEs) offer a potential solution by designing descriptors as probabilistic latent vectors derived from molecular structures. These models can be trained on large datasets, which have only molecular structures, and applied to transfer learning. Nevertheless, the approximate posterior distribution of the latent vectors of the usual VAE assumes a simple multivariate Gaussian distribution with zero covariance, which may limit the performance of representing the latent features. To overcome this limitation, we propose a novel molecular deep generative model that incorporates a hierarchical structure into the probabilistic latent vectors. We achieve this by a denoising diffusion probabilistic model (DDPM). We demonstrate that our model can design effective molecular latent vectors for molecular property prediction from some experiments by small datasets on physical properties and activity. The results highlight the superior prediction performance and robustness of our model compared to existing approaches.

翻译：在数据驱动的药物发现中，分子描述符的设计是一项至关重要的任务。变分自编码器等深度生成模型通过将描述符设计为源自分子结构的概率潜在向量，提供了一种可能的解决方案。这些模型可以在仅包含分子结构的大型数据集上进行训练，并应用于迁移学习。然而，传统变分自编码器中潜在向量的近似后验分布假设为具有零协方差的多元高斯分布，这可能限制了其表示潜在特征的能力。为克服这一局限，我们提出了一种新颖的分子深度生成模型，该模型将层级结构融入概率潜在向量中。我们通过去噪扩散概率模型实现了这一目标。实验表明，我们的模型能够基于物理性质和活性相关的小型数据集，设计出有效的分子潜在向量用于分子属性预测。结果凸显了相较于现有方法，我们的模型在预测性能和鲁棒性方面具有显著优势。