State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
State Environmental Protection Key Laboratory of Ecological Effect and Risk Assessment of Chemicals, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Int J Mol Sci. 2023 Feb 1;24(3):2768. doi: 10.3390/ijms24032768.
Synthetic musks (SMs), as an indispensable odor additive, are widely used in various personal care products. However, due to their physico-chemical properties, SMs were detected in various environmental media, even in samples from arctic regions, leading to severe threats to human health (e.g., abortion risk). Environmentally friendly and functionally improved SMs have been theoretically designed in previous studies. However, the synthesizability of these derivatives has barely been proven. Thus, this study developed a method to verify the synthesizability of previously designed SM derivatives using machine learning, 2D-QSAR, 3D-QSAR, and high-throughput density functional theory in order to screen for synthesizable, high-performance (odor sensitivity), and environmentally friendly SM derivatives. In this study, three SM derivatives (i.e., D52, D37, and D25) were screened and recommended due to their good performances (i.e., high synthesizability and odor sensitivity; low abortion risk; and bioaccumulation ability in skin keratin). In addition, the synthesizability mechanism of SM derivatives was also analyzed. Results revealed that high intramolecular hydrogen bond strength, electrostatic interaction, qH value, energy gap, and low E would lead to a higher synthesizability of SMs and their derivatives. This study broke the synthesizability bottleneck of theoretically designed environment-friendly SM derivatives and advanced the mechanism of screening functional derivatives.
合成麝香 (SMs) 作为一种不可或缺的气味添加剂，广泛应用于各种个人护理产品中。然而，由于其物理化学性质，SMs 已在各种环境介质中被检测到，甚至在北极地区的样本中也被检测到，对人类健康造成了严重威胁（例如，流产风险）。在以前的研究中，已经从理论上设计了环保且功能改进的 SMs。然而，这些衍生物的可合成性几乎没有得到证明。因此，本研究开发了一种使用机器学习、二维定量构效关系 (2D-QSAR)、三维定量构效关系 (3D-QSAR) 和高通量密度泛函理论来验证以前设计的 SM 衍生物可合成性的方法，以筛选出可合成、高性能（气味敏感性）和环保的 SM 衍生物。在这项研究中，由于其良好的性能（即高可合成性和气味敏感性；低流产风险；以及皮肤角蛋白中的生物累积能力），筛选并推荐了三种 SM 衍生物（即 D52、D37 和 D25）。此外，还分析了 SM 衍生物的可合成性机制。结果表明，较高的分子内氢键强度、静电相互作用、qH 值、能隙和较低的 E 将导致 SMs 及其衍生物具有更高的可合成性。这项研究打破了理论上设计的环保 SM 衍生物的可合成性瓶颈，推进了功能衍生物筛选的机制。
