Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms

Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms

This study aims to significantly improve existing quantitative structure-property relationship (QSPR) models for predicting the octanol-water partition coefficient (KOW). This is because accurate predictions of KOW are crucial for assessing the environmental behavior and bioaccumulation potential of...

Full description

Saved in:
Bibliographic Details
Main Authors: Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-06-01
Series:Green Chemical Engineering
Subjects:
Octanol-water partition coefficient
Environmental property
QSPR
Interpretable model
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666952824000542
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2666952824000542

Similar Items