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Unveiling drug-induced osteotoxicity: A machine learning approach and webserver
Huizi Cui1, Yi He1, Zhibang Wang1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China.
Abstract:
Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These toxic effects are critical concerns in clinical practice, drug development, and environmental management. However, current toxicity assessment models lack specialized datasets and algorithms specifically designed to predict osteotoxicity In this study, we compiled a dataset of osteotoxic molecules and used clustering analysis to classify them into four distinct groups Furthermore, target prediction identified key genes (IL6, TNF, ESR1, and MAPK3), while GO and KEGG analyses were employed to explore the complex underlying mechanisms Additionally, we developed prediction models based on molecular fingerprints and descriptors. We further advanced our approach by incorporating models such as Transformer, SVM, XGBoost, and molecular graphs integrated with Weave GNN, ViT, and a pre-trained KPGT model. Specifically, the descriptor-based model achieved an accuracy of 0.82 and an AUC of 0.89; the molecular graph model reached an accuracy of 0.84 and an AUC of 0.86; and the KPGT model attained both an accuracy and an AUC of 0.86. These findings led to the creation of Bonetox, the first online platform specifically designed for predicting osteotoxicity. This tool aids in assessing the impact of hazardous substances on bone health during drug development, thereby improving safety protocols, mitigating skeletal side effects, and ultimately enhancing therapeutic outcomes and public safety.
Insights
This study developed Bonetox, an AI platform for predicting drug-induced osteotoxicity. It identifies harmful drug effects on bone health, improving drug safety and therapeutic outcomes.
Area of Science:
- Pharmacology
- Toxicology
- Computational Chemistry
Background:
- Drug-induced osteotoxicity presents significant risks in clinical practice and drug development.
- Existing toxicity assessment models lack specialized datasets and algorithms for predicting bone toxicity.
- There is a critical need for predictive tools to evaluate skeletal safety during pharmaceutical research.
Purpose of the Study:
- To develop a predictive model and an online platform for drug-induced osteotoxicity.
- To identify key molecular targets and pathways involved in osteotoxicity.
- To create a robust tool for assessing bone health risks associated with chemical compounds.
Main Methods:
- Compiled a dataset of osteotoxic molecules and performed clustering analysis.
- Utilized target prediction, Gene Ontology (GO), and KEGG pathway analyses.
- Developed and evaluated various machine learning models including Transformer, SVM, XGBoost, and graph neural networks (GNNs) with molecular graphs, ViT, and KPGT.
Main Results:
- Identified key genes (IL6, TNF, ESR1, MAPK3) and elucidated underlying mechanisms.
- Descriptor-based models achieved 0.82 accuracy and 0.89 AUC.
- Molecular graph and KPGT models demonstrated strong predictive performance with AUCs of 0.86.
- Launched Bonetox, the first online platform for osteotoxicity prediction.
Conclusions:
- Bonetox provides a valuable tool for assessing osteotoxicity during drug development.
- The platform aids in improving safety protocols and mitigating skeletal side effects.
- Enhanced prediction of bone toxicity contributes to safer therapeutics and public health.
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