.png){kind=icon}
Return
Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function
.png){kind=icon}
OA
AI
DOI：10.1021/acs.jcim.2c00041.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated disynthons in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model 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. 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 DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, 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 structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling 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.
Keywords:
SMALL-MOLECULE DISCOVERY
INHIBITORS
QSAR
DERIVATIVES
SELECTION
IDENTIFICATION
TECHNOLOGY
AFFINITY
UREA
Journal
IF:
5.3
Papers:
9.1K
Citations:
4.0W
Organization
Cited Papers
ShareSaveOne for all, all for one: neuro‐HIV multidisciplinary platform for the assessment and management of neurocognitive complaints in people living with HIV.png)
HIV Medicine
OAAI
HIV MedicineIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveSelection of Carbonic Anhydrase IX Inhibitors from One Million DNA-Encoded CompoundsACS CHEMICAL BIOLOGY
ACS CHEMICAL BIOLOGYIF3.8
PREAI
ShareSaveShareSave
View more