Computational toxicology involves the development and use of computer-based models to understand and predict the toxicity of harmful agents and their interactions with biological organisms and the environment. This field has been benefited from the computational power and chemical and biological information, currently available. This discipline aims to understand and quantify the relationships between chemical structures and their physicochemical or biological properties. These studies enable to predict toxicity values of new substances and, whenever possible, to better understand the single or multiple modes of action. Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models are increasingly used in toxicology, ecotoxicology, and pharmacology to make these predictions.  The information on this topic is scattered in the literature and a structured course for undergraduate students in Chemistry is missing. To contribute in this field, we developed an e-textbook on Computational Toxicology. The developed electronic textbook of the course will help the students to grasp the background knowledge of the methodologies involved. The e-textbook will be continuously updated with exercises and examples. At the end of the course the students will be able to: 1) search, classify and manage toxicological information found in the literature, 2) know the fundamentals of QSAR model development, and 3) understand the criterias for the validation of QSAR models for regulatory purposes.

Computational toxicology e-textbook: A tool for an undergraduate course to understand and use toxicological information to avoid risk

2019 Spring National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.