VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM.

Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in vehicles, it is critical to choose interior parts with low odor and VOC emissions. However, prevailing odor evaluation methods are subjective, costly, and potentially harmful to the health of evaluators.

Accurate prediction of K based on experimental measurement of K and computed physicochemical properties of candidate compounds in CNS drug discovery.

A mathematical equation model was developed by building the relationship between the f/f ratio and the computed physicochemical properties of candidate compounds, thereby predicting K based on a single experimentally measured K value. A total of 256 compounds and 36 marketed published drugs including acidic, basic, neutral, zwitterionic, CNS-penetrant, and non-CNS penetrant compounds with diverse structures and physicochemical properties were involved in this study. A strong correlation was demonstrated between the f/f ratio and physicochemical parameters (CLogP and ionized fraction).