首页|Reports from East China University of Science and Technology Highlight Recent Findings in Machine Learning (Attentiveskin: To Predict Skin Corrosion/irritation Potentials of Chemicals Via Explainable Machine Learning Methods)
Reports from East China University of Science and Technology Highlight Recent Findings in Machine Learning (Attentiveskin: To Predict Skin Corrosion/irritation Potentials of Chemicals Via Explainable Machine Learning Methods)
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NETL
NSTL
Amer Chemical Soc
Investigators publish new report on Machine Learning. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “Skin Corrosion/ Irritation (Corr./Irrit.) has long been a health hazard in the Globally Harmonized System (GHS). Several in silico models have been built to predict Skin Corr./Irrit. as an alternative to the increasingly restricted animal testing.” Funders for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission). The news reporters obtained a quote from the research from the East China University of Science and Technology, “However, current studies are limited by data amount/quality and model availability. To address these issues, we compiled a traceable consensus GHS data set comprising 731 Corr., 1283 Irrit., and 1205 negative (Neg.) samples from 6 governmental databases and 2 external data sets. Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. For 10-fold cross-validation, the best Corr. vs Neg. classifier achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 97.1%, while the best Irrit. vs Neg. classifier achieved an AUC of 84.7%. Compared with existing in silico tools on external validation, our Attentive FP classifiers showed the highest metrics on Corr. vs Neg. and the second highest accuracy on Irrit. vs Neg. The SHapley Additive exPlanation approach was further applied to figure out important molecular features, and the attention weights were visualized to perform interpretable prediction. Structural alerts associated with Skin Corr./Irrit. were also identified.
ShanghaiPeople’s Republic of ChinaAsiaChemicalsCyborgsEmerging TechnologiesMachine LearningEast China University of Science and Technology
NETL
NSTL
Amer Chemical Soc
