In-silico Models in Molecular Property and Toxicity Assessments

Aim and Objective: This study examined the idea of using in silico chemical toxicity prediction to prevent occupational cancer as well as potential developments in worker health in the future. This review examines a novel method for assessing the reliability of in silico predictions using raw data and discusses how to calculate the evaluation's confidence level based on the reliability of the data. I looked through a number of publications and books on the subject of in silico chemical toxicity and carcinogenicity prediction. The idea was to apply the most recent in silico prediction reports published after 2015.

Materials and Methods: While quantitative regression and qualitative classification studies have made extensive use of machine learning techniques, structural alerts can be thought of as an additional tool for lead optimization. This paper focused on the development of predictive models for different toxicities in the last few years. Web servers and databases were also made available. Even though the approaches and models are very beneficial for drug design, there are still certain obstacles and shortcomings that need to be addressed in order to enhance drug safety assessment in the future.

Conclusion: This review includes applications of In-silico models in molecular property and toxicity assessments. This also explains the role of In-silico models in carcinogenicity and mutagenicity, acute oral toxicity, cardiotoxicity, hepatotoxicity, irritation and corrosion, eco-toxicity etc.,

Novel concepts of in silico assessments like alternative tools for chemical toxic tests, in silico prediction for chemical toxicity, structural alert, in silico prediction for nanotoxicology and OECD toolbox for the prediction of chemical toxicity are also part of this review.