Application of Large Language Models in Drug-Induced Osteotoxicity Prediction

Drug-induced osteotoxicity refers to the harmful effects
certain
drugs have on the skeletal system, posing significant safety risks.
These toxic effects are a key concern in clinical practice, drug development,
and environmental management. However, existing toxicity assessment
models lack specialized data sets and algorithms for predicting osteotoxicity.
In our study, we collected osteotoxic molecules and employed various
large language models, including DeepSeek and ChatGPT, alongside traditional
machine learning methods to predict their properties. Among these,
the DeepSeek R1 and ChatGPT o3 models achieved ACC values of 0.87
and 0.88, respectively. Our results indicate that machine learning
methods can assist in evaluating the impact of harmful substances
on bone health during drug development, improving safety protocols,
mitigating skeletal side effects, and enhancing treatment outcomes
and public safety. Furthermore, it highlights the potential of large
language models in predicting molecular toxicity and their significance
in the fields of health and chemical sciences.