A5074977846- Computational Drug Discovery Methods
- Machine Learning in Materials Science
- Metabolomics and Mass Spectrometry Studies
- Liver Disease Diagnosis and Treatment
- Statistical and Computational Modeling
- Risk and Safety Analysis
- Drug-Induced Hepatotoxicity and Protection
Northumbria University
2024
Jeonbuk National University
2021-2022
Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs or chemicals, so pharmaceutical and chemical industries demand reliable precise computational tools to assess respiratory compounds. The purpose this study develop quantitative structure-activity relationship models for large dataset compounds associated with system toxicity. First, several feature selection techniques are explored find optimal subset molecular descriptors efficient modeling. Then,...
Drug-induced liver toxicity is one of the significant safety challenges for patient's health and pharmaceutical industry. It causes termination drug candidates in clinical trials also retractions approved drugs from market. Thus, it essential to identify hepatotoxic compounds initial stages development process. The purpose this study construct quantitative structure activity relationship models using machine learning algorithms systematical feature selection methods molecular descriptor...
Organ toxicity caused by chemicals is a serious problem in the creation and usage of such as medications, insecticides, chemical products, cosmetics. In recent decades, initiation development chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, removed from marketplace because significant toxicity. As result, it an urgent requirement develop silico models that can reliably anticipate this...
Abstract Chemical-induced pulmonary toxicity, characterized by adverse respiratory effects from various drugs or chemicals, is increasingly becoming a point of concern for the pharmaceutical and chemical sectors, as well public health. Traditional toxicity prediction methods are not only expensive but also demand significant time effort. In response to these challenges, we focus on computational models identify potential toxicants early in drug development process. Early identification...