Structure-based in silico modeling of chemically-induced toxicity

Evaluating the potential toxicity of chemical compounds is an important step in the development of most new products, ranging from drugs and food additives to packaging materials and cosmetics. Data on the toxicity of these compounds are often not available because a thorough experimental study of every candidate compound can be too expensive and time consuming to be feasible. It is thus necessary to develop computational models that can help to screen candidate compounds by predicting their toxicity using chemical structural information. These models are developed with the help of chemical informatics methods that allow structural information to be captured using chemical descriptors and used for modeling complex endpoints such as toxicity using machine learning techniques. In this research, we propose to generate novel chemical descriptors and develop a new machine learning technique for modeling. The novel descriptors are generated by fragmenting chemical structures into linear paths of connected atoms and annotating these with atom-based features such as atom identity and partial charge. These features provide flexibility in exploring different degrees of structural details from the same chemical fragment. These annotated chemical fragments are then used for modeling toxicity using a statistical method called as Markov chain analysis. The performance of this method is evaluated using previous experimental toxicity data on skin sensitization and Ames mutagenicity. The preliminary results show that the new method is able to capture more meaningful descriptors and yield higher prediction accuracies as compared to other methods. We plan to refine this method further by including additional annotation features, developing more complex Markov models, and exploring effects of co-occurring fragments. The goal of this refinement is to make the method more robust and increase its predictive performance based on validation against known datasets. This research will help to reduce the time and money required to bring new products to market by assisting researchers in the lab to prioritize experimental tests. This research may also lead to the discovery of new structural alerts related to chemical toxicity, thereby increasing our understanding of the mechanisms underlying these complex phenomena.