Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function

TL;DR

This paper introduces a regression-based method with an uncertainty-aware loss function for analyzing DNA-encoded library data, improving denoising and visualization of structure-activity relationships in drug discovery.

Contribution

It presents a novel Poisson-based negative log-likelihood loss function for DEL data, enabling uncertainty modeling and better SAR trend visualization compared to binary classifiers.

Findings

Effective denoising of DEL data using the proposed method.

Improved visualization of structure-activity relationships.

Model robustness by ignoring low-confidence outliers.

Abstract

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find small molecules that bind a protein target. Applying QSAR modeling to DEL data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been shown recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" to accommodate the sparse and noisy nature of DEL data. However, a binary classifier cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules using a custom negative log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships (SAR). Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a dataset of 108k compounds screened against CAIX, and a dataset of 5.7M compounds screened against sEH and SIRT2. Due to the treatment of uncertainty in the data through the negative log-likelihood loss function, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying SAR trends and enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.