IMPECCABLE: A Digital Twin Drug Discovery Workflow Comprising AI, Physics and Experiment & the Creation of Nanomolar-Potent Inhibitors

The traditional drug discovery process is slow, costly and prone to high failure rates. To address this, we have developed IMPECCABLE, an integrated, heterogeneous in silico workflow combining physics-based methods and artificial intelligence to explore small-molecule chemical space. Operating on Frontier and Aurora exascale supercomputers, the workflow rapidly screens, generates and evaluates candidate compounds. Promising candidates enter a generative, multi-objective active learning loop where reliable binding free energies from ESMACS and TIES guide REINVENT to predict diverse, novel and synthesizable molecules. Unlike conventional approaches, this tightly coupled architecture integrates hit identification together with hit-to-lead optimisation and expansion within a single automated pipeline. Crucially, and uniquely, IMPECCABLE also integrates human medicinal chemistry input based on experiments, creating a molecular digital twin. We applied IMPECCABLE to two disease-related targets, WDR91 and TNKS2, demonstrating its ability to produce viable binders, while also performing prospective experimental validation for WDR91. Following multi-objective optimisation via an iterative REINVENT-TIES cycle, we identified four potent hits for WDR91 exhibiting nanomolar binding affinity with KD values between 129 ± 1 nM and 287 ± 1 nM. Remarkably, these four hits emerged from a selection of just ten synthesized compounds, demonstrating an exceptional ca 40% success rate.