Administrative Supplement: Using machine learning to predict odor characteristics from molecular structure

行政补充：利用机器学习从分子结构预测气味特征

• 批准号: 10405294
• 负责人: Emily Jo Mayhew
• 金额: $ 0.25万
• 依托单位: MONELL CHEMICAL SENSES CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-04 至 2022-09-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405294
• 关键词: Address; Administrative Supplement; Analytical Chemistry; Characteristics; Chemical Structure; Chemicals; Chemistry; Classification; Collection; Complex; Consumption; Data; Data Set; Descriptor; Development; Evaluation; Food; Fruit; Gas Chromatography; Goals; Gold; Health Food; Hour; Human; Human Resources; Knowledge; Learning; Link; Machine Learning; Mass Fragmentography; Measurement; Measures; Methods; Modeling; Molecular Structure; National Institute on Deafness and Other Communication Disorders; Odors; Olfactory Pathways; Palate; Perception; Positioning Attribute; Procedures; Programmed Learning; Property; Protocols documentation; Psychophysics; Quality Control; Recipe; Research; Research Technics; Resolution; Resources; Sampling; Science; Scientist; Sensory; Smell Perception; Speed; Stimulus; Structure; Testing; Time; Training; Work; base; data quality; design; experience; food science; human subject; improved; machine learning algorithm; model building; predictive modeling; prevent; rapid technique; skills; sound

PROJECT SUMMARY/ABSTRACT We cannot yet look at a chemical structure and predict if the molecule will have an odor, much less what character it will have. The goal of the proposed research is to apply machine learning to predict perceptual characteristics from chemical features of molecules. The specific aims of the proposal will determine (1) which molecules are odorous , and (2) what data are needed to model odor character. Building a highly predictive model requires two key ingredients: high-quality data and a sound modeling approach. High-quality data must be accurate (ratings are consistent and describe true odor properties) and detailed (ratings describe even small differences in odor properties). We have collected human psychophysical data on a diverse set of molecules and have trained a model to predict if a molecule has an odor, but pilot data identified odorous contaminants that limit model training and measurement of model accuracy. In Aim 1, I will apply my background in analytical chemistry to evaluate the accuracy of the data, using gas chromatography to identify and correct errors caused by chemical contaminants. In Aim 2, I will apply my experience in human sensory evaluation to measure and compare the consistency and the degree of detail in ratings that can be achieved with different sensory methods and subject training procedures. By executing my training plan, I will develop the skills in statistical programming and machine learning needed to employ a sound modeling approach to these problems. The model constructed in Aim 1 will enable prediction of odor classification (odor/odorless) for any molecule and thus define which molecules are perceptually relevant. Predicting odor character is a far more complex challenge – while a molecule can have only one of two odor classifications (odor or odorless) it may elicit any number of diverse odor character attributes (fruity, floral, musky, sweet, etc.). Descriptive Analysis (DA) is the gold standard method for generating accurate and detailed sensory profiles, but this method is time-consuming. We estimate that an odor character dataset will be large enough (“model-ready”) to predict odor character with approximately 10,000 molecules and that it would require more than 30,000 hours of human subject evaluation, or approximately 6 years for the typical trained panel, to produce this dataset using DA. Before we invest the time and resources, it is responsible to evaluate the relative data quality of more rapid sensory methods. The results of Aim 2 are expected to determine the best approach for generating a model-ready dataset by quantifying trade-offs in degree of detail (data resolution), rating consistency, and method speed of five candidate sensory methods. Together, these aims represent a significant step forward in linking chemical recipe to human odor perception, an advancement that supports the NIDCD goal of understanding normal olfactory function (how stimulus relates to percept) and has many potential applications in foods (what composition of molecules should be present to produce a target aroma percept).

项目摘要/摘要 我们还不能查看化学结构，并预测该分子是否会有气味，更不用说什么 它将具有的角色。拟议的研究的目的是应用机器学习来预测感知 分子化学特征的特征。提案的具体目的将确定（1） 分子是有用的，（2）为气味特征建模所需的数据。建立高度预测性 模型需要两个关键的命令：高质量数据和一种声音建模方法。高质量数据必须 准确（评分是一致的，并描述真实的气味特性）并详细（评分甚至描述 气味特性的小差异）。我们已经收集了有关潜水员一组的人类心理物理数据 分子并已经训练了一个模型来预测分子是否有气味，但试验数据确定了有臭味的 限制模型训练和测量模型精度的污染物。在AIM 1中，我将应用我的 分析化学背景以评估数据的准确性，使用气相色谱法鉴定 并正确由化学污染物引起的错误。在AIM 2中，我将在人类感官中运用我的经验 评估以衡量和比较可以实现的评分的一致性和细节程度 采用不同的感官方法和主题培训程序。通过执行我的培训计划，我将制定 统计编程和机器学习的技能需要采用合理的建模方法 这些问题。在AIM 1中构建的模型将使气味分类（气味/无味）预测 任何分子，因此定义了哪些分子在感知上相关。预测气味特征是一个很远的 更复杂的挑战 - 虽然分子只能具有两个气味类之一（气味或无味） 可能会引起任何数量的潜水气味特征属性（果味，花卉，麝香，甜蜜等）。描述性 分析（DA）是生成准确和详细的感官曲线的黄金标准方法，但这 方法是耗时的。我们估计气味字符数据集将足够大（“模型就绪”） 预测大约10,000个分子的气味特征，并且需要超过30,000小时 人类受试者评估，或典型训练的面板大约6年来生产此数据集 使用DA。在我们投资时间和资源之前，它有责任评估 更快的感觉方法。 AIM 2的结果有望确定生成的最佳方法 通过详细程度（数据解决），评级一致性和 五种候选感觉方法的方法速度。这些目标在一起代表了向前迈出的重要一步 将化学配方与人类气味感知联系起来，这是一个支持NIDCD目标的进步 了解正常的嗅觉功能（刺激与感知如何相关），并且具有许多潜在的应用 在食物中（应该存在哪些分子组成以产生目标香气感知）。