Open Computing Grid for Molecular Sciences

Abstract

The number of chemicals in society is largely increasing, and therewith the risk of being exposed to chemicals increases. Knowledge of possible toxic effects of these chemicals is vital, as are the measurement and assessment of the effects and related risks. Within the European Union, the Registration, Evaluation, and Authorisation of Chemicals (REACH) legislation [1] places responsibility on the chemical industries to properly assess the risks associated with their products. It has been estimated that about 30,000 new chemicals will be put on the European market in the coming years. The assessment of these chemicals would cost billions of euros and involve the use of millions of animals. REACH also aims to ensure that risks from substances of very high concern (SVHC) are properly controlled or that the substances are substituted. To match REACH requirements, fast and reliable methods with reproducible results are crucial, and regulatory bodies would be able to approve results. Property prediction and modeling will play an important role in this case [2]. Toxicology, the study of harmful interactions between chemicals and biological systems [3], uses more and more computer models. These models are based on already available data and help to reduce in vivo testing. Toxicity modeling and its data have many applications such as characterizing hazards, assessing environmental risks, and identifying potential lead components in drug discovery. A well-established method for toxicity modeling is quantitative structure–activity relationship (QSAR) or quantitative structure–property relationship (QSPR) [4,5]. On the basis of the available measured and calculated properties or activities and descriptors of compounds, predictive models for a certain property are built, which are then used to predict that