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.

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.

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.

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.