Projection-based molecular feature maps for CNN-driven nephrotoxicity prediction.
Muhammad Zafar Irshad Khan1, Jia-Nan Ren1, Hong-Yu-Xiang Ye1
1College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, 310058, Zhejiang, China.
Archives of Toxicology
|December 8, 2025
Summary
This study introduces a novel projection method to predict kidney toxicity in drugs. This approach converts 3D molecular structures into 2D maps, improving the accuracy of predictive models for drug safety.
Area of Science:
- Computational chemistry
- Toxicology
- Drug development
Background:
- Predicting nephrotoxic agents is crucial for drug development due to kidney toxicity risks.
- Traditional molecular descriptors often lack the spatial and electronic details needed for accurate nephrotoxicity prediction.
Purpose of the Study:
- To develop a novel projection-based method for enhanced nephrotoxicity prediction.
- To improve feature representation and deep learning model performance using 3D to 2D molecular structure transformation.
Main Methods:
- Utilized Mollweide and Equirectangular projections to convert 3D molecular geometries into 2D maps.
- Incorporated atom-based, electrostatic, and van der Waals (vdW) projections to represent molecular properties.
- Developed a Convolutional Neural Network (CNN) model for prediction.
Main Results:
- The Mollweide projection achieved 83% predictive accuracy and an AUC of 0.86, outperforming other methods.
- Projection-based maps enhanced molecular pattern recognition by visualizing atomic positions, charge distribution, and steric potentials.
- The model's reliability was confirmed via independent testing, cross-validation, and comparison with traditional descriptor models.
Conclusions:
- Projection-based molecular representations show significant potential for effective nephrotoxicity screening.
- This approach offers advancements in toxicology prediction and contributes to improved drug safety.
