Can I read Prediction of Serious Eye Damage or Eye Irritation Potential of Compounds via Consensus Labelling Models and Active Learning Models Based on Uncertainty Strategies on EtoBox?

Prediction of Serious Eye Damage or Eye Irritation Potential of Compounds via Consensus Labelling Models and Active Learning Models Based on Uncertainty Strategies by Peiwen Di; Mingyue Zheng; Tianbiao Yang; Geng Chen; Jianan Ren; Xutong Li; Hualiang Jiang is a Medicine article available to read on EtoBox.

What is Prediction of Serious Eye Damage or Eye Irritation Potential of Compounds via Consensus Labelling Models and Active Learning Models Based on Uncertainty Strategies about?

Serious eye damage and eye irritation have been authenticated to be significant human health issues in various fields such as ophthalmic pharmaceuticals. Due to the shortcomings of traditional animal testing methods, in silico methods have advanced to study eye toxicity. The models for predicting serious eye damage and eye irritation potential of compounds were developed using 2299 and 5214 compounds, respectively. The 40 global single models and 40 local models were developed by combining 5 molecular description methods and 4 machine learning methods. The 40 active learning models were developed by adopting uncertainty-based active learning strategies and taking local models as initial models. The 110 global consensus models based on 40 global single models were developed using a consensus strategy. Active learning models and global consensus models performed high prediction accuracy. The test accuracy of the best serious eye damage model and eye irritation model reached 0.972 and 0.959, respectively. The applicability domains for all models were calculated to verify the rationality of prediction effect. In addition, 8 structural alerts probably causing serious eye damage or eye i

Who reads Prediction of Serious Eye Damage or Eye Irritation Potential of Compounds via Consensus Labelling Models and Active Learning Models Based on Uncertainty Strategies?

Author

Peiwen Di; Mingyue Zheng; Tianbiao Yang; Geng Chen; Jianan Ren; Xutong Li; Hualiang Jiang

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Medicine (Life Sciences)