Background: The application of deep generative models for molecular discovery has witnessed a significant surge in recent years. Currently, the field of molecular generation and molecular optimization is predominantly governed by autoregressive models regardless of how molecular data is represented. However, an emerging paradigm in the generation domain is diffusion models, which treat data non-autoregressively and have achieved significant breakthroughs in areas such as image generation. Methods: The potential and capability of diffusion models in molecular generation and optimization tasks remain largely unexplored. In order to investigate the potential applicability of diffusion models in the domain of molecular exploration, we proposed DiffSeqMol, a molecular sequence generation model, underpinned by diffusion process. Results & Discussion: DiffSeqMol distinguishes itself from traditional autoregressive methods by its capacity to draw samples from random noise and direct generating the entire molecule. Through experiment evaluations, we demonstrated that DiffSeqMol can achieve, even surpass, the performance of established state-of-the-art models on unconditional generation tasks and molecular optimization tasks. Conclusion: Taken together, our results show that DiffSeqMol can be considered a promising molecular generation method. It opens new pathways to traverse the expansive chemical space and to discover novel molecules.

中文翻译：

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DiffSeqMol：一种基于非自回归扩散的分子序列生成和优化方法

背景：近年来，深度生成模型在分子发现中的应用出现了显着增长。目前，分子生成和分子优化领域主要由自回归模型控制，无论分子数据如何表示。然而，生成领域的一个新兴范式是扩散模型，它以非自回归方式处理数据，并在图像生成等领域取得了重大突破。方法：扩散模型在分子生成和优化任务中的潜力和能力在很大程度上仍未得到探索。为了研究扩散模型在分子探索领域的潜在适用性，我们提出了 DiffSeqMol，一种以扩散过程为基础的分子序列生成模型。结果与讨论：DiffSeqMol 与传统自回归方法的区别在于它能够从随机噪声中抽取样本并直接生成整个分子。通过实验评估，我们证明了 DiffSeqMol 在无条件生成任务和分子优化任务上可以实现甚至超越已建立的最先进模型的性能。结论：总而言之，我们的结果表明 DiffSeqMol 可以被认为是一种有前途的分子生成方法。它开辟了穿越广阔化学空间和发现新分子的新途径。