  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.
