In this review we address to what extent computational techniques can augment our ability
to predict toxicity. The first section provides a brief history of empirical observations on …

Early prediction of safety issues in drug development is at the same time highly desirable
and highly challenging. Recent advances emphasize the importance of understanding the …

Drug discovery is complex and expensive. Numerous drug candidates fail late in clinical
trials or even after being released to the market. These failures are not only due to …

Introduction: Despite rapid progress in OMICs and computational technologies in compound
safety assessment, drug failure rate due to toxicity is still unacceptably high. One reason for …

We thank Dix and colleagues for taking the time to consider and respond to our recent article
on predicting in vivo hazard using high-throughput in vitro screening (Thomas et al., 2012) …

Xenobiotic metabolism has evolved as a key protective system of organisms against
potentially harmful chemicals or compounds typically not present in a particular organism …

Machine learning (ML) models to predict the toxicity of small molecules have garnered great
attention and have become widely used in recent years. Computational toxicity prediction is …

Computational toxicology (Figure 1) is an actively developing area of research which
complements and extends traditional experimental toxicology. Computational toxicology …

Humans are exposed to a multitude of substances such as manufactured products,
therapeutic agents, or environmental toxicants that perturb essential biological mechanisms …

I first heard about computational toxicology about ten years ago when I was asked to apply
elsewhere modeling predictive strategies, typical of rational drug design. This was the way I …