Скачать или смотреть EUCYS – PATEN: Predicting Acute Toxicity with Ensemble Neural Networks

Scientist: Daniel Golshmid

Accurate assessment of the toxicity of chemical compounds is crucial in ensuring their safety and efficacy in the field of drug discovery and development. Artificial neural networks are machine learning models that learn patterns within data and make predictions on new data. This project aims to develop a model that combines several neural networks to accurately predict the toxicity of molecules. The model was trained on molecules with known toxicity values and was tested on new data, showing promising predictive abilities. The project is innovative in its combination of computational chemistry and advanced machine learning for acute toxicity prediction, potentially advancing the field of drug development, which currently relies mainly on animal testing for toxicity assessment.