التفاصيل البيبلوغرافية
| العنوان: |
A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction |
| المؤلفون: |
Rajas Ketkar, Yue Liu, Hengji Wang, Hao Tian |
| المصدر: |
International Journal of Molecular Sciences; Volume 24; Issue 15; Pages: 11966 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2023 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
graph neural network, acute toxicity, attentive FP, machine learning |
| جغرافية الموضوع: |
agris |
| الوصف: |
With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this study, five graph models (message-passing neural network, graph convolution network, graph attention network, path-augmented graph transformer network, and Attentive FP) were applied on four toxicity tasks (fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri). With the lowest prediction error, Attentive FP was reported to have the best performance in all four tasks. Moreover, the attention weights of the Attentive FP model helped to construct atomic heatmaps and provide good explainability. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Molecular Informatics; https://dx.doi.org/10.3390/ijms241511966 |
| DOI: |
10.3390/ijms241511966 |
| الاتاحة: |
https://doi.org/10.3390/ijms241511966 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.8839129C |
| قاعدة البيانات: |
BASE |