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

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.

Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network.

Identifying the taste characteristics of molecules is essential for the expansion of their application in health foods and drugs. It is time-consuming and consumable to identify the taste characteristics of a large number of compounds through experiments. To date, computational methods have become an important technique for identifying molecular taste. In this work, bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener are predicted using three structure-taste relationship models based on the convolutional neural networks (CNN), multi-layer perceptron (MLP)-Descriptor, and MLP-Fingerprint. The results showed that all three models have unique characteristics in the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener. For the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener, the MLP-Fingerprint model exhibited a higher predictive AUC value (0.94, 0.94 and 0.95) than the MLP-Descriptor model (0.94, 0.84 and 0.87) and the CNN model (0.88, 0.90 and 0.91) by external validation, respectively. The MLP-Descriptor model showed a distinct structure-taste relationship of the studied molecules, which helps to understand the key properties associated with bitterants and sweeteners. The CNN model requires only a simple 2D chemical map as input to automate feature extraction for favorable prediction. The obtained models achieved accurate predictions of bitterant/non-bitterant, sweetener/non-sweetener and bitterant and sweetener, providing vital references for the identification of bioactive molecules and toxic substances.

Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends.

Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.

Identification of the functional food ingredients with antithrombotic properties via virtual screen and experimental studies.

Thrombin is a key therapeutic target protein of thrombosis. To date, massive studies have focused on the exploration of antithrombotic compounds. Here we capitalize on molecular docking, molecular simulations and spectroscopic experiments for virtually screening natural products that can inhibit thrombin and elucidating their interaction mechanism. Six compounds are screened from a natural product database by a cross-analysis based on two semi-flexible molecular docking methods. We show that four compounds can effectively inhibit thrombin and Calceolarioside B is the most competitive one based on enzyme inhibition experiments. Moreover, the binding free energies of these compounds with thrombin exhibit a consistent rank trend with their enzyme inhibition assay results. In addition, the Van der Waals is the main force to drive the interaction between the ligands and the receptor, which can be deduced from the fluorescence spectral results. This work provides a new insight into the development of antithrombotic natural compounds.