A resource-efficient structure-based workflow for fragment progression enables parallel hit discovery and validation of functionally diverse modulators of NCS-1 protein-protein interactions.

Efficient drug discovery workflows ideally generate data that directly address key translational milestones, including confirmation of target engagement and binding pose, structure-activity relationships (SARs), and biological relevance, within rapid and resource-efficient experimental cycles. Here, we describe a data-driven, automation-assisted, customizable framework for fragment-to-hit progression that directly delivers structurally validated hit series primed for rapid SAR exploration by exploiting the high-throughput crystallography available at synchrotrons. This recently evolved direct-to-biology approach combines X-ray crystallographic fragment screening with algorithmically-guided fragment merging and reagent prioritization; low-cost robotic array synthesis and reaction production assessment by LC-MS; and finally orthogonal biophysical evaluation of crude reaction mixtures for binding assessment and 3D binding pose using grating-coupled interferometry and crystallography, respectively. We demonstrated the effectiveness of the strategy on a challenging target class by collectively progressing a large set of fragment hits through a single DMTA cycle comprising over 250 synthetically diverse compounds, enabling rapid, resource- and cost-effective exploration of the off-catalogue chemical space. This led to the discovery of protein-protein interaction modulators of Neuronal Calcium Sensor 1 (NCS-1), a key regulator in the central nervous system with therapeutic relevance, which contains a large interaction pocket capable of accommodating multiple protein partners. We advanced fragments into scaffold series that selectively engage biologically validated subpockets and, importantly, revealed allosteric and cryptic binding sites, critical for achieving specificity in target modulation and subsequent hit-to-lead generation. The approach is general, engineerable and scalable and provides proof of principle for how to expand the scope of fragment-based hit discovery.