Computational Toxicology

Abstract: The discipline of “computational” toxicology has been studied thoroughly and its applications have been implemented in the field to the advantage of many individuals by providing cheap and rapid solutions for chemical screening.The current review focuses on the recent progress, obstacles, and use of computational toxicology with respect to various predicted within toxicity endpoints; carcinogenicity, mutagenicity and reproductive spanning toxicity in males. The combination of these approaches involving quantitative structure-activity relationship (QSAR) alongside machine learning (ML) and molecular docking methods has contributed to the improvement of the accuracy of the predictions. There are clear opportunities in the area of computional toxicity for example reduction in testing on animals and more rapid evaluation of the chemical safety of substances as well as improved drug design and in the recognition of threats. Another research will be focused on the integration of multi-omic data, accurate and interpretable model development, and also extension in applicability to new fields such as nanotoxicology. In this regard, computational toxicology allows factories and companies to accommodate only less toxic or more pharmaceutically active chemicals or drugs. The development of computer-aided drug designing alongside experimental screening practices has greatly enhanced methods used in evaluating the safety of chemicals and developing new drugs