Vol. 6, Issue 2, Part B (2024)

The integration of artificial intelligence (AI) into virtual screening (VS) is transforming drug discovery by enhancing the speed, accuracy, and efficiency of identifying potential therapeutic candidates. Traditional VS approaches, while valuable, are often limited by extensive computational demands and inherent biases in data. AI-powered models, including machine learning (ML) and deep learning (DL) architectures, offer innovative solutions by enabling rapid analysis of large chemical libraries and improving predictions for molecular interactions and drug-likeness. This paper examines the methods and applications of various AI techniques-such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and reinforcement learning (RL)-in virtual screening and evaluates their impact on accelerating the drug discovery pipeline. However, the implementation of AI-based VS systems also faces significant challenges, including data quality, model interpretability, and computational resource requirements, which must be addressed for wider adoption. Additionally, ethical and regulatory considerations are crucial for responsible AI application in drug development. By advancing these AI-driven approaches and establishing best practices, virtual screening can become a more reliable and accessible tool in the quest for new therapeutic solutions.