From millions to molecules: how DEL-ML workflows transform hit identification

Early hit identification is still a bottleneck for many discovery teams. Classical high throughput screening requires large amounts of protein, expensive assay set up, and still samples only a tiny part of chemical space. As targets become more challenging and timelines tighter, teams look for methods that can explore billions of structures without exploding budgets or cycle times. DNA encoded libraries and machine learning are emerging as a practical way to do exactly that.

A DNA encoded library (DEL) is a collection of small molecules, each tagged with a unique DNA barcode. This tag records the synthetic history of the molecule and later helps identify it. DEL technology allows researchers to screen up to billions of compounds in one tube, under conditions tailored to the biology of a specific target. After selection, binders are pulled down, their DNA tags are sequenced, and enrichment patterns show which chemotypes interact with the protein. Compared with classical screening, DEL compresses logistics and opens access to much broader structural diversity.

The key step in modern DEL workflows is no longer just enrichment ranking but data interpretation. Sequencing output from DEL selections forms a large, structured dataset that can be labeled using statistical methods. Machine learning models trained on this data learn non obvious structure activity relationships that are difficult to see with simple counts and fold change thresholds.

In advanced implementations, like the approach described at https://chem-space.com/drug-discovery-cro/del-ml-cs-approach, these models are then applied to huge external chemical spaces that combine in stock and on demand compounds. Instead of synthesizing thousands of follow up analogs, a team can let the model score hundreds of millions of virtual candidates and select a focused set of molecules with the highest predicted probability of binding.

A typical DEL-ML workflow starts from the initial selection and sequencing. Data is cleaned, normalized, and used to train classification or regression models that estimate binding likelihood. The next step is to search large combinatorial spaces and prioritize series that match both the model and project constraints such as physicochemistry or novelty. Only a limited number of compounds, often in the hundreds, is synthesized and tested to validate the predictions.

For discovery leaders, DEL-ML workflows are not just a technical upgrade, they shift how portfolios are managed. Access to billions of structures in a controlled, data rich setting creates new options for targets that were previously considered low priority or too risky. Machine learning on DEL data supports more informed go or no go decisions and helps align chemistry resources with the most promising directions.

In practice, the combination of DEL and ML does not replace medicinal chemistry or biology. It gives these teams a better starting point, a clearer map of chemical space around their target, and a faster route from millions of virtual structures to a small, testable set of molecules with real potential to become drug candidates.

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