Structure-based virtual screening of ultra-large chemical spaces: Advances and pitfalls

On-demand chemical spaces consist of molecules that are, a priori , readily synthesizable from sets of commercial building blocks through robust organic reactions. As these spaces expand—now reaching the scale of several trillions of compounds—computational chemists are compelled to develop innovative algorithms for efficient enumeration, storage, and virtual screening, particularly when three-dimensional constraints of target proteins are involved. This review examines the primary approaches to structure-based ultra-large virtual screening, highlighting the significant advantages of screening at such a scale while addressing the remaining practical and theoretical hurdles. Current prospective applications, often relying on brute-force docking, typically report improved hit rates and more potent primary hits; however, they must contend with the exponential growth of available chemical space. To address this, recent developments have integrated active learning, probabilistic sampling, and synthon-guided methods to accelerate docking and prioritize the most promising compounds. Finally, we provide a perspective on the transformative impact of ultra-large chemical spaces on early hit identification and the overall organization of early drug discovery. • Ultra-large chemical spaces are revolutionizing early hit identification. In drug discovery. • Novel algorithms, such as machine learning-accelerated and synthon-based docking, enable efficient screening of trillion-sized libraries. • Virtual screening of ultra-large chemical spaces leads to higher hit rates and more potent hits. • On-demand accessibility of these spaces streamlines the transition from hit identification to structure-activity relationship (SAR) studies