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

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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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Jian-Wang Liu (22085230) (author)
مؤلفون آخرون: 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)
منشور في: 2025
الموضوعات:
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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