Computational Prediction of Drug Toxicity and Binding Affinity

Abstract

This study focuses on the computational prediction of drug toxicity and binding affinity, two critical aspects in the drug development process. Computational models offer a promising approach to predict these parameters accurately and efficiently, reducing the need for extensive in vitro and in vivo testing. This research leverages advanced machine learning algorithms and molecular docking simulations to predict the toxicity and binding affinity of drug candidates. By integrating various biochemical and pharmacological data, the study aims to develop robust predictive models that can identify potential toxic effects and optimal binding affinities early in the drug discovery pipeline. The results demonstrate that computational predictions can effectively complement traditional methods, offering significant advantages in terms of cost, time, and resource savings. This study provides valuable insights into the development of safer and more effective drugs, highlighting the potential of computational approaches in modern pharmacology.