# DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries

arXiv: https://arxiv.org/abs/2212.00136 · HTML: https://mlsanity.com/paper/2212.00136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Protein-Ligand Scoring with Convolutional Neural Networks](https://mlsanity.com/paper/1612.02751/) (2016)
- [AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery](https://mlsanity.com/paper/1510.02855/) (2015)
- [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://mlsanity.com/paper/2005.07704/) (2020)
- [Machine learning on DNA-encoded libraries: A new paradigm for hit-finding](https://mlsanity.com/paper/2002.02530/) (2020)
- [Partial Product Aware Machine Learning on DNA-Encoded Libraries](https://mlsanity.com/paper/2205.08020/) (2022)
- [libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications](https://mlsanity.com/paper/1912.04822/) (2019)

## Built on by (most cited first, 3 of 3)

- [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)

## Abstract (arXiv)

DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL screens measure protein binding affinity though sequencing reads of molecules tagged with unique DNA-barcodes that survive a series of selection experiments. Computational models have been deployed to learn the latent binding affinities that are correlated to the sequenced count data; however, this correlation is often obfuscated by various sources of noise introduced in its complicated data-generation process. In order to denoise DEL count data and screen for molecules with good binding affinity, computational models require the correct assumptions in their modeling structure to capture the correct signals underlying the data. Recent advances in DEL models have focused on probabilistic formulations of count data, but existing approaches have thus far been limited to only utilizing 2-D molecule-level representations. We introduce a new paradigm, DEL-Dock, that combines ligand-based descriptors with 3-D spatial information from docked protein-ligand complexes. 3-D spatial information allows our model to learn over the actual binding modality rather than using only structured-based information of the ligand. We show that our model is capable of effectively denoising DEL count data to predict molecule enrichment scores that are better correlated with experimental binding affinity measurements compared to prior works. Moreover, by learning over a collection of docked poses we demonstrate that our model, trained only on DEL data, implicitly learns to perform good docking pose selection without requiring external supervision from expensive-to-source protein crystal structures.