Insight into the Structure–Odor Relationship of Molecules: A Computational Study Based on Deep Learning

Abstract

Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure–odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules.

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

“…Lötsch et al [25] showed applications of olfactometric data with a set of unsupervised and supervised algorithms for pattern-based odor detection and recognition, odor prediction from physicochemical properties of volatile molecules, and knowledge discovery in publicly available big databases. Two different approaches to predict the structure-odor relationship with machine learning were conducted by Schicker et al [26]-who developed a classification algorithm that quantitatively assigns structural patterns to odors-and Bo et al [27], who used deep learning on the structural features for a binary two-class prediction of the odors. It was possible for Lee et al [28] to generate a principal odor map by constructing a message passing an artificial neural network (NN) to map chemical structures to odor percepts that enable odor quality prediction with human-level odor description performance and outperform chemoinformatic models.…”

Mlp4green: A Binary Classification Approach Specifically for Green Odor

“…With the results of machine learning, the study will further explore the chemical structure and mechanism of action of green odor molecules, revealing the potential laws of their functioning. Additionally, by using an internet service that is freely available to everyone, the binary classifiers developed through the application of machine learning can be employed, saving time and money [ 12 , 13 , 14 , 15 , 16 , 17 ]. This study presents a new breakthrough in the field of green odor research by applying machine learning for the first time to green odor molecular prediction.…”

OWSum – Algorithmic Odor Prediction and Insight into Structure-Odor-Relationship

“…For the creation of new odorants, a predictive approach is necessary during molecular design to reduce the space of candidate molecules from virtually anything to a promising range of molecule structures. Though many advances in odor prediction have been achieved in recent years [8][9][10][11][12][13][14][15][16][17][18], we unfortunately still know little about the relationship between a molecule's structure and its odor [19][20][21] to an extent where we can provide chemists with a toolbox for designing molecular structures with a specific odor in mind. However, sophisticated computational methods have led to new insights into these relationships [22,23] and allow prediction whether a molecule is odorous at all [24].…”