IN-SILICO TOXICITY PREDICTION TOOLS: A REVIEW OF TECHNIQUES AND APPLICATIONS

A vital tool in drug discovery and development, in-silico toxicity prediction tools and algorithms provide economical, time-efficient and morally sound substitutes for traditional in-vitro and in-vivo toxicological testing. Computational techniques are essential to predict the possible toxicity and side effects prior to clinical testing because of the ever-increasing complexity of pharmaceutical compounds and the regulatory bodies’ increasing focus on early toxicity screening. An overview of the in-silico methods for toxicity prediction is discussed in this article, which includes pharmacophore modeling, machine learning algorithms, molecular docking, and Quantitative Structure–Activity Relationship (QSAR) models. It weighs the underlying principles, prediction powersand practical applications of popular database tools including ProTox-II, SwissADME, Toxtree, and ADMETlab. The review also includes case studies that show how the tools are used to evaluate the toxicity of potential drugs. Limitations including model generalizability, lack of standardization, and validation issues persist despite the tool’s increasing dependability. The final section of the analysis looks at the state of regulations today and suggests ways to improve the accuracy and acceptability of in-silico toxicity forecasts in the pharmaceutical sector.