ARTIFICIAL INTELLIGENCE FOR DRUG DISCOVERY

Abstract

Drug discovery is a long and complex process because it involves multiple stages of research, development, and testing to ensure the safety and efficacy of a new drug before it can be approved for clinical use.AI (Artificial Intelligence) has become an important tool in drug discovery due to its ability to analyze vast amounts of data and identify patterns and relationships that might not be apparent to humans.The developed bioactivity predictor app uses Quantitative Structure-Activity Relationship (QSAR) model to predict the bioactivity against the target Cyclooxygenase-2 (COX-2) which helps to identify potential drug molecules that may be effective in treating diseases.COX-2 has been identified as an important target in QSAR studies due to the potential for COX-2 inhibitors to treat a variety of diseases such as cancer, Alzheimer's disease, and cardiovascular disease.By using AI, drug discovery researchers can identify potential drug candidates more efficiently and at a lower cost compared to traditional methods.This can accelerate the lead optimization process and reduce the number of compounds that need to be tested in vitro or in vivo.