Scalable Framework for logP Prediction
Published:Dec 31, 2025 05:32
•1 min read
•ArXiv
Analysis
This paper presents a significant advancement in logP prediction by addressing data integration challenges and demonstrating the effectiveness of ensemble methods. The study's scalability and the insights into the multivariate nature of lipophilicity are noteworthy. The comparison of different modeling approaches and the identification of the limitations of linear models provide valuable guidance for future research. The stratified modeling strategy is a key contribution.
Key Takeaways
- •Developed a scalable framework for logP prediction using a large curated dataset.
- •Identified the importance of molecular weight as a predictor using SHAP analysis.
- •Demonstrated the superiority of tree-based ensemble methods over linear models.
- •Achieved optimal performance with a stratified modeling strategy.
- •Showed that descriptor-based ensemble models are competitive with graph neural networks.
Reference
“Tree-based ensemble methods, including Random Forest and XGBoost, proved inherently robust to this violation, achieving an R-squared of 0.765 and RMSE of 0.731 logP units on the test set.”