Chemical Toxicity Prediction Based on Artificial Intelligence: A review | ||||
| International Journal of Applied Intelligent Computing and Informatics | ||||
| Volume 1, Issue 1, May 2025, Page 1-8 PDF (987.2 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijaici.2025.330535.1000 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Eman Shehab  1; Hamada Nayel2; Mohamed Taha 3
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| 1Department of Computer Science, Faculty of Computers and Artificial Intelligence, University of Sadat City Sadat, Egypt | ||||
| 2Faculty of Computers and Artificial Intelligence, Benha University | ||||
| 3Faculty of Computers & Artificial Intelligence, Benha University | ||||
| Abstract | ||||
| The increasing number of chemicals has aroused public concern due to their negative influence on the environment and human health. To protect the environment and human health, the toxicity of these compounds must be assessed. Traditional in vitro and in vivo toxicity testing are time-consuming, expensive, and complex, and they may pose ethical considerations as well. Due to these restrictions, alternative methods for assessing the toxicity of a chemical are required. Numerous toxicity prediction models have been developed recently using a variety of machine learning and deep learning algorithms such as support vector machines, random forests, k-nearest neighbors, ensemble learning, and deep neural networks by integrating classical ML techniques or Deep Learning (DL) with molecular representations such as fingerprints or 2D graphs. This paper presents an overview of chemical toxicity and the drug Discovery Process. It summarizes current ML and DL models for predictive toxicology with a brief objective and the limitations and challenges AI faces in toxicity prediction. | ||||
| Keywords | ||||
| Chemical Toxicity; Drug Discovery Process; Molecular Representations; Machine Learning; Deep Learning | ||||
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