The Impact of Artificial Intelligence on Pharmacy Education, Research and Practice

Abstract

Artificial intelligence (AI) is transforming the present drug development and design method by tackling the obstacles addressed at each stage. AI improves the effectiveness of processes greatly by improving accuracy, reducing time and cost, using high-performance algorithms, and enabling computer-aided drug design (CADD). Efficient drug screening strategies are critical for discovering possible hit compounds among enormous amounts of data in compound repositories. The use of AI in drug development, comprising the screening of hit compounds as well as lead molecules, has shown to be more successful than traditional in vitro screening methods. This article examines advances in drug screening approaches made by AI-enhanced usage, machine learning (ML), along deep learning (DL) algorithms. It concentrates on AI applications for drug development, including screening methodologies and lead optimization approaches including quantitative structure-activity relationship (QSAR) modeling, pharmacophore modeling, de novo drug development, along high-throughput virtual screening. The discussion includes valuable insights into several parts of the drug screening process, focusing on the role of AI-based tools, pipelines, and case studies in reducing the intricacies of drug development.

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