XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost–PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship. Communicated by Ramaswamy H. Sarma

解析分子结构与气味的关联关系始终是一项极具挑战性的课题。探究分子结构与其对应气味之间的相关性时，核心挑战在于语言描述的气味描述符存在模糊性与晦涩性，尤其当气味分子来源各异时。随着机器学习（Machine Learning, ML）技术的飞速发展，机器学习与数据分析技术已被广泛应用于化学领域的定量构效关系（Quantitative Structure-Activity Relationship, QSAR）研究，以开展传统爱迪生式试错法难以实现的知识发现工作。气味分子的嗅觉感知正是上述难题之一，因为与其他感官相比，嗅觉是人类认知程度最低的感官之一。本研究构建了基于XGBoost的气味预测模型，可通过气味分子的SMILES字符串对其气味类别进行分类。我们首先收集了包含7种基础气味描述符的1278个气味分子数据集，并计算了所有分子的1875项理化性质。为筛选出有效理化特征，本研究还采用了主成分分析（Principal Component Analysis, PCA）特征降维算法。在独立测试集上进行验证时，本研究构建的机器学习模型可实现7种基础气味的高精度预测，其精确率与召回率均超过99%。本研究结果还与三项近期发表的同类研究进行了对比，结果显示，XGBoost-PCA模型在常见气味描述符预测任务中的表现优于其他对比模型。本研究构建的方法与机器学习模型，可为解析结构-气味关联关系提供有效助力。本文由Ramaswamy H. Sarma转交。