What Matters More in DNA-Encoded Libraries: Size, Quality or Chemical Diversity?

Key Takeaways

• X‑Chem limits DEL cycles to two‑three for lead‑like space
• Libraries under a billion molecules produced all clinical candidates
• Chemical diversity defined by geometry, topology, and building‑block selection
• High‑quality DELs improve machine‑learning data reliability
• Fewer amide bonds correlate with favorable physicochemical properties

Summary

DNA‑encoded libraries (DELs) enable billions of compounds to be screened, but X‑Chem argues that size alone does not predict success. By limiting synthesis to two‑three cycles, the company creates lead‑like molecules (350‑450 Da) with high three‑dimensional diversity, and its analysis of over 700 licensed families shows all clinical candidates came from libraries under a billion members. Dr. Ying Zhang emphasizes that chemical diversity, physicochemical properties, and intentional design drive reliable screening data and downstream machine‑learning applications. The discussion also outlines criteria for evaluating DEL partners beyond sheer library size.

Pulse Analysis

DNA‑encoded libraries have transformed early‑stage drug discovery by allowing billions of small molecules to be screened in a single assay. The technology relies on split‑and‑pool synthesis, where each additional reaction‑encoding cycle inflates the numerical size of the library. However, each extra cycle raises molecular weight, reduces synthetic efficiency, and generates side‑products that push compounds out of the lead‑like, drug‑like chemical space. Consequently, sheer library size is a misleading proxy for success; the true value lies in the quality and relevance of the molecules presented to the target.

X‑Chem has deliberately shifted its design philosophy toward libraries built with only two to three synthetic cycles, yielding compounds averaging 350–450 Da and containing fewer than one amide bond. This restraint preserves lead‑like physicochemical profiles while still achieving broad three‑dimensional and topological diversity through atom‑efficient reactions and varied building‑block geometry. The company’s internal analysis of more than 700 licensed families shows that every clinical candidate originated from libraries under one billion members, underscoring how high‑quality, diverse DELs accelerate hit identification, reduce cycle time, and generate robust data for machine‑learning models.

For partners evaluating DEL providers, the focus should move beyond catalog size to metrics such as defined chemical diversity, average molecular weight, and the frequency with which hits progress to follow‑up programs. Publications and disclosed compounds serve as transparent indicators of a library’s novelty and property distribution. As the industry increasingly integrates DEL data into AI‑driven discovery pipelines, libraries that deliver consistent, drug‑like chemistry become strategic assets, lowering risk and enabling faster translation from hit to clinical candidate. X‑Chem’s approach exemplifies this shift, setting a benchmark for quality‑first DEL design.