Computational Toxicology

Abstract

Computational toxicology played an important role in the ongoing paradigm shift in the field of toxicology. Computation toxicology is inherently a multidisciplinary field, and it comprises the building of models of many different types with different techniques. This article explains a variety of model building techniques and various supporting resources needed to build high-quality predictive models. The article is divided into four broad areas: (1) computational toxicology resources, (2) computational toxicology projects, (3) computational toxicology modeling, and (4) future directions and perspectives. All areas are composed around common themes of developmental toxicology and direct and indirect input from various computational toxicological analysis and integrative modeling. Predictive models also benefit from multiple data types and various available knowledge bases that play an important role from comprehensive understanding of toxicity. An integrative approach using, for example, biological pathways to inform feature selection for models has capability to balance statistical performance and a better understanding of modes of action. Due to these advantages, computational toxicology will be an indispensable cornerstone in future advances in toxicological sciences.