Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery.

Abstract

Deep learning has revolutionized virtual screening in drug discovery, offering unprecedented improvements in hit identification, lead optimization, and molecular property prediction. Traditional virtual screening methods, including structure-based docking and ligand-based screening, often suffer from computational inefficiencies, poor generalization, and reliance on predefined molecular descriptors. Deep learning addresses these limitations by leveraging graph neural networks (GNNs), transformer-based models, generative AI, and reinforcement learning to discover novel drug candidates more efficiently. This chapter explores the systematic deep learning pipeline for virtual screening, covering data acquisition, molecular representation learning, model architectures, training strategies, and uncertainty estimation. We discuss advanced model optimization techniques, including curriculum learning, transfer learning, and adversarial training, which enhance predictive accuracy and robustness. Despite significant progress, challenges remain, particularly in data quality, model interpretability, generalization to novel chemical spaces, and computational cost. Emerging trends, such as self-supervised learning, quantum computing for molecular simulations, and AI-integrated automated laboratories, are paving the way for the next generation of AI-driven drug discovery. By integrating machine learning with experimental validation, AI-powered virtual screening is set to accelerate early-stage drug discovery, reducing costs and improving the efficiency of therapeutic development.