【摘要】 气味感知是生物体解析化学信号的关键过程之一,其本质是气味分子与嗅觉受体的特异性结合引发的神经激活过程。然而,分子结构与其引发的嗅觉感知之间的非线性映射关系尚未完全阐明,传统感官评价方法的主观性和低通量限制了该领域的发展。借助机器学习技术进行分子结构与气味的定量构效关系建模是一种解决上述问题的有效方法。作者系统地梳理了基于定量构效关系的分子气味预测研究进展,重点探讨了数据资源、分子表征建模方法以及跨学科融合三个核心维度的技术路线,并对未来分子气味预测与可解释性人工神经网络技术等的融合进行了展望。更多还原

【Abstract】 Odor perception, a critical process for organisms to decode chemical signals, involves the specific binding of odor molecules to olfactory receptors, triggering neural activation. However, the nonlinear mapping between molecular structures and resulting olfactory perceptions remains incompletely elucidated. Traditional sensory evaluation methods face limitations due to subjectivity and low throughput, hindering progress in this field. Machine learning has emerged as a powerful tool to address these challenges by modeling quantitative structure-activity relationships. This review systematically reviews progress in molecular odor prediction using quantitative structure-activity relationships approaches, focusing on three core technical dimensions: data resources, molecular representation modeling methods, and interdisciplinary integration strategies. Future prospects are discussed for integrating molecular odor prediction with quantum chemistry calculations, single-cell sequencing, and interpretable artificial neural networks to enhance model transparency and biological relevance.更多还原