Machine learning model enhances human-centered drug toxicity prediction; Dr. Min-hyuk Park says it will be a practical tool in new drug development

A machine learning model developed by Dr. Min-hyuk Park significantly enhances the prediction of drug toxicity, an innovation that could transform drug development.
Validation of the model involved data from 434 hazardous and 790 approved drugs, establishing strong links between genetic characteristics and toxicity-related drug failures.
The model's predictive accuracy surpasses existing technologies, marking it as a pioneering advancement in understanding genotype-phenotype relationships relevant to drug safety.
Park emphasized, 'The human-centered toxicity prediction model will be a very practical tool in new drug development.' This cutting-edge approach promises to evolve as more data is incorporated, leading to increased effectiveness in preclinical evaluations.
The implications of this model could drastically reduce risks associated with drug trials and expedite the introduction of safer pharmaceuticals to the market.

A new machine learning model developed by Dr. Min-hyuk Park aims to enhance drug toxicity predictions, identifying dangerous drugs before clinical trials. The model outperformed existing systems and is anticipated to significantly facilitate the drug development process, according to Park and collaborator Woomin Song.