DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries

TL;DR

DEL-Ranking is a novel denoising framework that improves the accuracy of molecular affinity predictions in DNA-encoded library screening by correcting read count noise and learning causal features, supported by new datasets and strong empirical results.

Contribution

The paper introduces DEL-Ranking, a distribution-correction denoising method with a new ranking loss and iterative self-training, along with three comprehensive DEL datasets for AI research.

Findings

DEL-Ranking outperforms existing methods in correlation metrics.

It achieves zero-shot generalization across protein targets.

The model successfully identifies binding affinity motifs.

Abstract

DNA-encoded library (DEL) screening has revolutionized the detection of protein-ligand interactions through read counts, enabling rapid exploration of vast chemical spaces. However, noise in read counts, stemming from nonspecific interactions, can mislead this exploration process. We present DEL-Ranking, a novel distribution-correction denoising framework that addresses these challenges. Our approach introduces two key innovations: (1) a novel ranking loss that rectifies relative magnitude relationships between read counts, enabling the learning of causal features determining activity levels, and (2) an iterative algorithm employing self-training and consistency loss to establish model coherence between activity label and read count predictions. Furthermore, we contribute three new DEL screening datasets, the first to comprehensively include multi-dimensional molecular representations, protein-ligand enrichment values, and their activity labels. These datasets mitigate data scarcity issues in AI-driven DEL screening research. Rigorous evaluation on diverse DEL datasets demonstrates DEL-Ranking's superior performance across multiple correlation metrics, with significant improvements in binding affinity prediction accuracy. Our model exhibits zero-shot generalization ability across different protein targets and successfully identifies potential motifs determining compound binding affinity. This work advances DEL screening analysis and provides valuable resources for future research in this area.