QSAR in Safety Evaluation and Risk Assessment

Table of Contents

1. QSAR facilitating safety evaluation and risk assessment

Part I: Methods and Advances of QSAR
2. Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity
3. Deep learning-based descriptors as input for QSAR
4. Decision Forest A machine learning algorithms for QSAR modeling
5. Integrated modelling for compound efficacy and safety assessment
6. Deep learning QSAR methods for chemical toxicity prediction and risk assessment
7. Predictive modeling approaches for the risk assessment of persistent organic pollutants: Classical to Machine learning based QSAR Models
8. Machine learning based QSAR for safety evaluation
9. Advances in QSAR through Artificial Intelligence and Machine Learning methods
10. Advances of the QSAR approach as an alternative strategy in the Environmental Risk Assessment
11. QSAR modeling based on graph neural networks

Part II: Tools and Data Sources for QSAR
12. Modeling safety and risk assessment with VEGA HUB
13. Recent advancements in QSAR and Machine Learning Approaches for risk assessment of organic chemicals
14. admetSAR a valuable tool for assisting safety evaluation
15. QSAR tools for toxicity prediction in risk assessment a comparative analysis
16. Fast and Efficient Implementation of Computational Toxicology Solutions Using the FlexFilters Platform
17. Annotate a standard dataset for drug-induced liver injury to support developing QSAR models
18. Application of QSAR Models Based on Machine Learning Methods in Chemical Risk Assessment and Drug Discovery
19. EADB The database providing curated data for developing QSAR models to facilitate assessment of endocrine activity
20. Centralized data sources and QSAR methods for the prediction of idiosyncratic adverse drug reaction

Part III: QSAR models for Safety Evaluation of Drugs and Consumer Products
21. QSAR modeling for predicting drug-induced liver injury
22. The need of QSAR methods to assess safety of chemicals in food contact materials
23. QSAR models for predicting in vivo reproductive toxicity
24. Aryl hydrocarbon receptors and their ligands in human health management
25. Use of in silico protocols to evaluate drug safety
26. QSAR models for predicting cardiac toxicity of dugs

Part IV: QSAR models for Risk Assessment of Chemicals
27. Similarity-based analyses for the false-positive and false-negative chemicals on the second Ames/QSAR international challenge project
28. QSAR Model of Photolysis Kinetic Parameters in Aquatic Environment
29. QSAR models on transthyretin disrupting effects of chemicals
30. QSAR models for toxicity assessment of multicomponent systems
31. Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms
32. QSAR models for prediction of carrying capacity of microplastic towards organic pollutants
33. QSAR models on degradation rate constants of atmospheric pollutants

Part V: QSAR models in Material Science and Other Areas
34. Significance of QSAR in cancer risk assessment of polycyclic aromatic compounds (PACs)
35. QSAR in risk assessment of nanomaterials
36. In silico and in vitro ecotoxicity QSAR based predictions for the aquatic environment
37. In vitro to in vivo Extrapolation Methods in Chemical Hazard Identification and Risk Assessment
38. QSAR models in marine ecotoxicology

Authors

Huixiao Hong Supervisory Research Chemist - Division of Bioinformatics and Biostatistics, National Central for Toxicological Research (NCTR) at US Food and Drug Administration (FDA), USA. Huixiao Hong received his PhD from Nanjing University in China and conducted research at Maxwell Institute in Leeds University, England. He was an associate professor and the Director of Laboratory of Computational Chemistry at Nanjing University in China, a visiting scientist at the National Cancer Institute (NCI) at National Institutes of Health (NIH), a research scientist at Sumitomo Chemical Company in Japan. Huixiao Hong joined National Central for Toxicological Research (NCTR) at the U.S. Food and Drug Administration (FDA) in 2000. He is an SBRBPAS expert and the Chief of Bioinformatics Branch at NCTR/FDA. He is an associate editor of Experimental Biology and Medicine, Frontiers in Artificial Intelligence, and Frontiers in Bioinformatics, as well as editorial board member of several scientific journals. He has over 250 publications with over 15,000 citations and a Google Scholar H-index 63.