# Machine learning on DNA-encoded libraries: A new paradigm for hit-finding

arXiv: https://arxiv.org/abs/2002.02530 · HTML: https://mlsanity.com/paper/2002.02530/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 15 of 15)

- [RGFN: Synthesizable Molecular Generation Using GFlowNets](https://mlsanity.com/paper/2406.08506/) (2024)
- [Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift](https://mlsanity.com/paper/2111.12951/) (2021)
- [A graph representation of molecular ensembles for polymer property prediction](https://mlsanity.com/paper/2205.08619/) (2022)
- [Differentiable Physics: A Position Piece](https://mlsanity.com/paper/2109.07573/) (2021)
- [Generative Active Learning for the Search of Small-molecule Protein Binders](https://mlsanity.com/paper/2405.01616/) (2024)
- [Towards DNA-Encoded Library Generation with GFlowNets](https://mlsanity.com/paper/2404.10094/) (2024)
- [Partial Product Aware Machine Learning on DNA-Encoded Libraries](https://mlsanity.com/paper/2205.08020/) (2022)
- [DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries](https://mlsanity.com/paper/2212.00136/) (2022)
- [Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries](https://mlsanity.com/paper/2409.05916/) (2024)
- [Compositional Deep Probabilistic Models of DNA Encoded Libraries](https://mlsanity.com/paper/2310.13769/) (2023)
- [KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors](https://mlsanity.com/paper/2410.08938/) (2024)
- [DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries](https://mlsanity.com/paper/2410.14946/) (2024)
- [Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function](https://mlsanity.com/paper/2108.12471/) (2021)
- [Enhancing selectivity using Wasserstein distance based reweighing](https://mlsanity.com/paper/2401.11562/) (2024)
- [JEDEL: Zero-Shot DNA-Encoded Library Design for Early-Stage Drug Discovery](https://mlsanity.com/paper/2606.23745/) (2026)

## Abstract (arXiv)

DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up to billions of unique small molecules. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds. We train models using only DEL selection data and apply automated or automatable filters with chemist review restricted to the removal of molecules with potential for instability or reactivity. We validate this approach with a large prospective study (nearly 2000 compounds tested) across three diverse protein targets: sEH (a hydrolase), ERα (a nuclear receptor), and c-KIT (a kinase). The approach is effective, with an overall hit rate of ~30% at 30 textmuM and discovery of potent compounds (IC50 <10 nM) for every target. The model makes useful predictions even for molecules dissimilar to the original DEL and the compounds identified are diverse, predominantly drug-like, and different from known ligands. Collectively, the quality and quantity of DEL selection data; the power of modern machine learning methods; and access to large, inexpensive, commercially-available libraries creates a powerful new approach for hit finding.