Structure-Activity-Relationships can be directly extracted from high-throughput crystallographic evaluation of fragment elaborations in crude reaction mixtures

Fragment hits serve as starting points for lead-like compound development. A common approach to advancing from fragments is to build structure-activity relationships (SARs) from close analogues. One strategy involves performing automated chemistry around fragment hits and evaluating resulting crude reaction mixtures (CRMs) of analogues in assays, bypassing costly purification. However, these purification-agnostic workflows are often perceived as noisy, and therefore typically involve additional hit resynthesis for confirmation, potentially discarding false negatives and reducing SAR dataset size. High-throughput (HT) X-ray crystallography has the potential to address these issues by unambiguously resolving hits directly from 100s–1000s of CRMs. However, no systematic analytics exist for extracting SAR models from HT crystallographic evaluation of CRMs. We demonstrate that crystallographic SAR (xSAR) can be extracted from CRMs evaluated via HT X-ray crystallography. We present here a simple rule-based ligand scoring scheme that identifies conserved chemical features linked to binding and non-binding observations in crystallography. Applied to a large-scale crystallographic dataset of 957 fragment elaborations in CRMs targeting PHIP(2), a therapeutically relevant bromodomain, our xSAR model demonstrated effectiveness in two proof-of-concept experiments. First, it recovered 26 missed binders in the initial dataset, doubling the hit rate and denoising the dataset. Second, it enabled a prospective virtual screen, also leveraging previously resolved cocrystal structures, that identified novel hits with informative chemistries, achieving up to a 10-fold binding affinity improvement over the repurified hit from the initial CRM evaluation. This work establishes a proof-of-concept that xSAR models can be directly extracted from large-scale crystallographic readouts of CRMs, offering a valuable methodology to build SAR models and accelerate design-make-test iterations without requiring CRM hit resynthesis and confirmation. This invites future work to utilise advanced analytics and modelling techniques to further strengthen purification-agnostic workflows.