School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Int J Mol Sci. 2024 Jun 25;25(13):6940. doi: 10.3390/ijms25136940.
Deep generative models are becoming a tool of choice for exploring the molecular space. One important application area of deep generative models is the reverse design of drug compounds for given attributes (solubility, ease of synthesis, etc.). Although there are many generative models, these models cannot generate specific intervals of attributes. This paper proposes a AC-ModNet model that effectively combines VAE with AC-GAN to generate molecular structures in specific attribute intervals. The AC-ModNet is trained and evaluated using the open 250K ZINC dataset. In comparison with related models, our method performs best in the FCD and Frag model evaluation indicators. Moreover, we prove the AC-ModNet created molecules have potential application value in drug design by comparing and analyzing them with medical records in the PubChem database. The results of this paper will provide a new method for machine learning drug reverse design.
深度生成模型正成为探索分子空间的首选工具之一。深度生成模型的一个重要应用领域是针对给定属性（溶解度、合成难易度等）反向设计药物化合物。虽然有许多生成模型，但这些模型无法生成特定属性区间。本文提出了一种 AC-ModNet 模型，该模型有效地将 VAE 与 AC-GAN 相结合，生成特定属性区间内的分子结构。AC-ModNet 使用开放的 250K ZINC 数据集进行训练和评估。与相关模型相比，我们的方法在 FCD 和 Frag 模型评估指标上表现最佳。此外，我们通过将生成的分子与 PubChem 数据库中的病历进行比较和分析，证明了 AC-ModNet 创造的分子在药物设计中具有潜在的应用价值。本文的研究结果将为机器学习药物反向设计提供一种新方法。
