Mlp4green: A Binary Classification Approach Specifically for Green Odor.

Area of Science:

• Biochemistry
• Computational Chemistry
• Machine Learning

Background:

• Green odor, the scent of fresh leaves, possesses antibacterial properties and influences insect behavior.
• Current methods for evaluating green odor molecules are limited, hindering research.
• Machine learning (ML) offers potential for predicting molecular attributes.

Purpose of the Study:

• To develop a standardized method for identifying green odor molecules using ML.
• To explore the molecular characteristics and mechanisms of action of green odor compounds.
• To create a predictive tool for green odor classification.

Main Methods:

• Trained ML models on green odor molecules.
• Performed clustering analysis and molecular docking.
• Compared four ML algorithms, including Multilayer Perceptron (MLP).
• Utilized difference analysis to compare green and non-green odor molecules.

Main Results:

• MLP demonstrated superior performance in accuracy, precision, and other key metrics.
• Green odor molecules were found to have lower molecular mass and fewer electrons compared to non-green odor molecules.
• A binary classification prediction website for green odors was successfully developed using the MLP algorithm.

Conclusions:

• This study pioneers the application of deep learning in green odor research, ushering in an era of intelligent and standardized analysis.
• The developed ML model provides a robust tool for identifying and understanding green odor molecules.
• The findings offer new insights into the chemical properties and biological functions of green odors.