Predicting Dose-Response Success from Primary Screening Data
Abstract
High-throughput drug screening uses inexpensive primary screens to select compounds for more costly follow-up dose-response experiments, which estimate compound potency (pXC50) by determining the concentration required to produce a specified biological effect. However, many promoted compounds fail to yield a reportable potency, even when activity is subsequently confirmed. We investigated whether inexpensive three-point primary-screening data could predict whether a more costly 11-point dose-response experiment would yield a reportable pXC50. Using a dataset of 32,971 compound-assay pairs with three-point primary-screening and 11-point dose-response measurements, we trained random-forest models to predict successful potency quantification. Our model achieved an AUROC of 0.939 +/- 0.002 and generalized to previously unseen chemical scaffolds and assay-mechanism families. We found that primary-screen response features were more predictive of dose-response quantifiability than molecular structure-based features. Successful potency quantification was also strongly associated with primary-screen response amplitude. Across eight chronological test releases, the model retrospectively retained 90% of reportable potencies while selecting only 55.7% of historically promoted compound-assay pairs for follow-up profiling. These findings suggest that predicting potency quantifiability from primary-screening data could improve the efficiency of compound prioritization and reduce unnecessary dose-response experiments.
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BibTeX
@misc{lim2026predicting,
title = {{Predicting Dose-Response Success from Primary Screening Data}},
author = {Sean Lim},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.26538},
eprint = {2608.26538},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.26538},
note = {CC0},
}
RIS
TY - UNPB
TI - Predicting Dose-Response Success from Primary Screening Data
AU - Sean Lim
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2608.26538
UR - https://arxiv.org/abs/2608.26538
AB - High-throughput drug screening uses inexpensive primary screens to select compounds for more costly follow-up dose-response experiments, which estimate compound potency (pXC50) by determining the concentration required to produce a specified biological effect. However, many promoted compounds fail to yield a reportable potency, even when activity is subsequently confirmed. We investigated whether inexpensive three-point primary-screening data could predict whether a more costly 11-point dose-response experiment would yield a reportable pXC50. Using a dataset of 32,971 compound-assay pairs with three-point primary-screening and 11-point dose-response measurements, we trained random-forest models to predict successful potency quantification. Our model achieved an AUROC of 0.939 +/- 0.002 and generalized to previously unseen chemical scaffolds and assay-mechanism families. We found that primary-screen response features were more predictive of dose-response quantifiability than molecular structure-based features. Successful potency quantification was also strongly associated with primary-screen response amplitude. Across eight chronological test releases, the model retrospectively retained 90% of reportable potencies while selecting only 55.7% of historically promoted compound-assay pairs for follow-up profiling. These findings suggest that predicting potency quantifiability from primary-screening data could improve the efficiency of compound prioritization and reduce unnecessary dose-response experiments.
ER -