Advances in Artificial Intelligence (AI)-assisted approaches in drug screening

Abstract

Artificial intelligence (AI) is revolutionizing the current process of drug design and development, addressing the challenges encountered in its various stages. By utilizing AI, the efficiency of the process is significantly improved through enhanced precision, reduced time and cost, high-performance algorithms and AI-enabled computer-aided drug design (CADD). Effective drug screening techniques are crucial for identifying potential hit compounds from large volumes of data in compound repositories. The inclusion of AI in drug discovery, including the screening of hit compounds and lead molecules, has proven to be more effective than traditional in vitro screening assays. This article reviews the advancements in drug screening methods achieved through AI-enhanced applications, machine learning (ML), and deep learning (DL) algorithms. It specifically focuses on AI applications in the drug discovery phase, exploring screening strategies and lead optimization techniques such as Quantitative structure-activity relationship (QSAR) modeling, pharmacophore modeling, de novo drug designing, and high-throughput virtual screening. Valuable insights into different aspects of the drug screening process are discussed, highlighting the role of AI-based tools, pipelines, and case studies in simplifying the complexities associated with drug discovery.

Keywords

• Artificial intelligence
• Machine learning
• Hit compounds
• Drug discovery
• High throughput virtual screening
• Pharmacophore modeling