Uncertainty-Aware Deep Learning and Structural Feature Analysis for Reliable Nephrotoxicity Prediction

Nephrotoxicity remains a critical safety concern in drug development and clinical practice. Despite their significance, existing computational models for nephrotoxicity prediction face challenges related to limited precision and reliability. To address these challenges, this study constructed the la...

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Bibliographic Details
Main Author:	Jian-Wang Liu (22085230) (author)
Other Authors:	Ke-Yi Liu (22085233) (author), You-Chao Deng (18176233) (author), Shao-Hua Shi (14226552) (author), Xiang-Zheng Fu (22085236) (author), Yue-Ping Jiang (744321) (author), Jing Fang (170284) (author), Qing Zhang (1802) (author), De-Jun Jiang (18176230) (author), Shao Liu (399749) (author), Dong-Sheng Cao (399743) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified structural feature analysis provide actionable insights offer valuable tools identify structural alerts critical safety concern comprising 1831 high aware deep learning mean kappa value model also achieved analysis integrated dataset existing computational models uncertainty quantification method safer drug design established prediction models existing models uncertainty quantification kappa metric results demonstrate representative model related compounds quality nephrotoxicity molecular graphs model ’ limited precision key global highest performance effective drugs drug development clinical practice chemopy2d descriptors centric techniques centric methods beneficial transformations applicability domain alongside model 4 %. 3 %.
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Uncertainty-Aware Deep Learning and Structural Feature Analysis for Reliable Nephrotoxicity Prediction

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