# JEDEL: Zero-Shot DNA-Encoded Library Design for Early-Stage Drug Discovery

arXiv: https://arxiv.org/abs/2606.23745 · HTML: https://mlsanity.com/paper/2606.23745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://mlsanity.com/paper/2205.07249/) (2022)
- [3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction](https://mlsanity.com/paper/2303.03543/) (2023)
- [Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design](https://mlsanity.com/paper/2110.06389/) (2021)
- [Projecting Molecules into Synthesizable Chemical Spaces](https://mlsanity.com/paper/2406.04628/) (2024)
- [Machine learning on DNA-encoded libraries: A new paradigm for hit-finding](https://mlsanity.com/paper/2002.02530/) (2020)
- [ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design](https://mlsanity.com/paper/2411.04130/) (2024)
- [Activity Cliff Prediction: Dataset and Benchmark](https://mlsanity.com/paper/2302.07541/) (2023)
- [Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design](https://mlsanity.com/paper/2505.10545/) (2025)
- [KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors](https://mlsanity.com/paper/2410.08938/) (2024)

## Abstract (arXiv)

We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representations of active ligands. JEDEL is the first model to map pharmacophore interaction patterns to actionable, scalable synthesis instructions, enabling the design of targeted libraries comprising potentially millions of molecules. Unlike existing generative approaches that produce virtual compounds requiring downstream synthesis planning, JEDEL operates within the space of purchasable building blocks and validated reactions, ensuring that every output is experimentally realizable by construction. JEDEL learns a predictive alignment between pharmacophore geometry and molecular structure and decodes this into combinatorial synthesis routes at scale. Across 18 protein targets, it generates focused libraries that outperform random and diversity-based baselines in predicted binding affinity, pharmacophore recovery, and sample efficiency, without target-specific retraining. JEDEL enables a shift from virtual molecule generation to experimentally deployable library design.