Abstract：
Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.
摘要：
孕烷X受体(PXR),在与消化和代谢相关的人体组织中广泛表达，负责识别和解毒人类遇到的多种外源性物质。为了理解PXR的混杂性质及其结合多种配体的能力，计算方法，viz.,定量结构-活性关系(QSAR)模型，有助于潜在毒理学试剂的快速复制，并减少用于建立有意义的监管决策的动物数量。适应更大数据集的机器学习技术的最新进展有望帮助开发复杂混合物的有效预测模型(即。,膳食补充剂)在进行深入实验之前。500个结构不同的PXR配体用于开发传统的二维（2D）QSAR，基于机器学习的2D-QSAR，基于场的三维（3D）QSAR，和基于机器学习的3D-QSAR模型建立预测性机器学习方法的效用。此外,建立了激动剂的适用域，以确保生成稳健的QSAR模型。膳食PXR激动剂的预测集用于外部验证生成的QSAR模型。QSAR数据分析显示，机器学习3D-QSAR技术在预测外部萜烯的活性方面更准确，外部验证平方相关系数（R2）为0.70，而机器学习2D-QSAR中的R2为0.52。此外,从现场3D-QSAR模型收集了PXR结合袋的视觉摘要。通过在本研究中开发多个QSAR模型，已经为评估各种化学骨架的PXR激动作用奠定了坚实的基础，以期鉴定复杂混合物中的潜在病原体。由RamaswamyH.Sarma沟通。
