ArticleJournal of chemical information and modeling2025
Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.Nature communications · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uncertainty quantification (UQ) has been recognized as a prerequisite for reliable and trustworthy computational modeling in drug discovery. Two widely considered paradigms, Bayesian methods (deep ensemble and MC dropout) and evidential learning, differ in their computational demands and expressivity of uncertainties, excelling in complementary settings. Here, we propose hybrid approaches that combine both paradigms and benchmark them on the Papyrus++ data set across two end points (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.