The origins of computational toxicology trace back to the 1960s and 1970s when early
quantitative structure–activity relationship, or QSAR, models were developed. These models aimed to predict the biological activity of chemicals based on their molecular structures. Advances in computational power during this period allowed for increasingly sophisticated
simulations and analyses, laying the groundwork for modern computational approaches. The 1980s and 1990s saw the expansion of the field with the advent of
molecular docking,
cheminformatics, and
bioinformatics tools. The rise of
high-throughput screening technologies provided vast datasets, which fueled the need for computational methods to manage and interpret complex toxicological data. In the early 21st century, the establishment of initiatives such as the U.S.
Environmental Protection Agency's, or EPA's,
ToxCast program marked a significant milestone. ToxCast aimed to integrate computational and experimental data to improve toxicity prediction and reduce reliance on
animal testing. During this time, advances in
machine learning and
artificial intelligence further transformed the field, enabling the analysis of large-scale datasets and the development of predictive models with greater accuracy. Today, computational toxicology continues to evolve, driven by innovations in omics technologies,
big data analytics, and regulatory science. It plays a crucial role in risk assessment, drug development, and
environmental protection, offering faster and more
ethical alternatives to traditional toxicological testing. == References ==