Generative Access to Make-on-Demand Chemical Space Enables Ultra-Large Virtual Screening

Authors

Zhang, K., Sun, Y., Li, X., Wang, Y., Peng, C., Jin, X., Hu, Q., Huang, J.

Abstract

Ultra-large-scale virtual screening (ULVS) increasingly relies on make-on-demand chemical spaces, but efficient access to these spaces remains challenging because their scale far exceeds what can be exhaustively enumerated and docked. Here, we present REAL-SWIT, a generative ULVS framework that learns Enamine REAL Space as a synthesizable molecular distribution and couples this generator with a target-specific scoring model to enable docking-guided exploration of REAL Space at the level of complete molecule. The generative model learned transferable features of the REAL Space distribution: approximately 96% of generated molecules were found in REAL Space, and a subset of generated molecules absent from the training release appeared in later expanded releases. In computational benchmarks, REAL-SWIT identified more molecules with favorable docking scores than representative fragment-based search strategies, including cooperative building-block combinations that fragment-level prioritization tended to miss. Experimental validation for ROCK1 yielded six biochemical inhibitors among 23 synthesized compounds, including RX-3 with an IC50 of 0.17 M. These results establish generative access to make-on-demand chemical space as a practical strategy for ULVS.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 24 Sep 2026.

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