AI-Driven Drug Discovery: Computational Methods and Applications

Artificial intelligence (AI) and its revolutionary effects on the medication development process. The project explores the potential integration of artificial intelligence tools into the different phases of drug development using state-of-the-art computational methods. This project’s overarching goal is to assess how well target selection, lead compound optimization, and toxicity prediction are served by data analytics, predictive modelling, and machine learning algorithms. This research aims to analyze contemporary uses and breakthroughs in artificial intelligence (AI) to better understand how it improves drug discovery pipeline efficiency and accuracy. Beyond this, it delves into the challenges and opportunities of AI-driven drug discovery, with an emphasis on finding fresh approaches to old biomedical problems. The dynamic environment at the crossroads of AI and pharmaceutical sciences is better-understood thanks to this comprehensive analysis. It opens the door to more efficient drug development processes and the development of new therapeutic approaches.