GoKawiilChEMBL34 mined for 191,732 ligand pairs to build a random baseline for optimization
Researchers filtered the PostgreSQL version of the CHEMBL34 database to find pairs of compounds tested against the same protein target, assay, activity type and publication, where one molecule was a simple chemical modification of the other (such as swapping a hydrogen for fluorine or a carbon for nitrogen). This produced 191,732 parent-analogue pairs, which the team supplemented by synthesizing new analogues from 18 selected parent compounds using RDKit-driven enumeration of feasible single-atom substitutions, each capped at $400 per 10 mg.
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Medicinal chemists routinely make small structural tweaks hoping to improve potency, but without a statistical baseline it's hard to know whether observed improvements are meaningful or just noise. By cataloguing a huge, real-world set of these single-atom modifications and their measured activity changes, the work creates a reference distribution against which future ligand optimization efforts can be judged for actual skill versus chance.
- Nearly 192,000 parent-analogue compound pairs were extracted from ChEMBL34 based on strict criteria like shared assay, target and publication.
- Eighteen parent compounds were used to computationally generate and then chemically synthesize new analogues via defined atom substitutions.
- Statistical methods including bootstrap resampling and Clopper-Pearson estimation were used to build confidence intervals for rare or zero-frequency outcomes.
Source: nature.com — Xu, 2026-09-16
Published there as: “Development of a random background to understand ligand optimization”
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