Ai-Driven Drug Response Prediction: Bridginggeno Mics and Personalized Cancer Treatment

The advent of personalized medicine has highlighted  the importance of predicting drug sensitivity in cancer  treatment. This paper presents a predictive modeling approach that leverages genomic profiles and ML methods to forecast a  sensitivity of cancer cells to various drugs. By analyzing a comprehensive dataset encompassing genomic features, drug  characteristics, and response metrics, we develop a robust model to estimate drug effectiveness. Our methodology involves  Predicting the natural log of the half-maximal inhibitory concentration (LN_IC50), a crucial  indicator of drug sensitivity,  requires preprocessing the data, choosing pertinent features, and training several machine  learning models . A results  demonstrate  the potential of integrating genomic information with advanced analytics to enhance the precision of drug  response predictions, thereby contributing to the creation of more efficient and customized   cancer therapies.