Diff4VS：基于分类器引导扩散的虚拟筛选用于HIV抑制分子生成
Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening
AI总结：
本文提出Diff4VS模型，首次结合分类器引导扩散与基于配体的虚拟筛选生成HIV抑制分子，并引入DrugIndex评估指标及发现分子生成退化现象，为药物设计提供新方法。
摘要：
艾滋病流行已导致4000万人死亡，并引发了严重的全球性问题。识别新的HIV抑制分子对于对抗艾滋病流行至关重要。在此，分类器引导扩散模型与基于配体的虚拟筛选策略首次被结合，用于发现潜在的HIV抑制分子。我们将其称为Diff4VS。使用HIV分子数据集训练了一个额外的分类器，并利用该分类器的梯度来引导扩散模型生成HIV抑制分子。实验表明，与其他方法相比，Diff4VS能够生成更多的候选HIV抑制分子。受基于配体的虚拟筛选启发，提出了一种新的评估指标DrugIndex。DrugIndex是生成分子中候选药物分子的比例与训练集中候选药物分子比例的比值。DrugIndex为从药学角度演进分子生成模型提供了一种新的评估方法。此外，我们报告了在使用分子生成模型进行虚拟筛选时观察到的一个新现象。与真实分子相比，生成的分子中与已知药物分子高度相似的比例较低。我们将其称为分子生成中的退化现象。基于数据分析，这种退化现象可能源于生成模型中生成具有特定结构分子的困难性。我们的研究从方法、指标和现象分析三个方面为生成模型在药物设计中的应用做出了贡献。
英文摘要：
The AIDS epidemic has killed 40 million people and caused serious global problems. The identification of new HIV-inhibiting molecules is of great importance for combating the AIDS epidemic. Here, the Classifier Guidance Diffusion model and ligand-based virtual screening strategy are combined to discover potential HIV-inhibiting molecules for the first time. We call it Diff4VS. An extra classifier is trained using the HIV molecule dataset, and the gradient of the classifier is used to guide the Diffusion to generate HIV-inhibiting molecules. Experiments show that Diff4VS can generate more candidate HIV-inhibiting molecules than other methods. Inspired by ligand-based virtual screening, a new metric DrugIndex is proposed. The DrugIndex is the ratio of the proportion of candidate drug molecules in the generated molecule to the proportion of candidate drug molecules in the training set. DrugIndex provides a new evaluation method for evolving molecular generative models from a pharmaceutical perspective. Besides, we report a new phenomenon observed when using molecule generation models for virtual screening. Compared to real molecules, the generated molecules have a lower proportion that is highly similar to known drug molecules. We call it Degradation in molecule generation. Based on the data analysis, the Degradation may result from the difficulty of generating molecules with a specific structure in the generative model. Our research contributes to the application of generative models in drug design from method, metric, and phenomenon analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
