ArticleJournal of chemical information and modeling2026
Defining the Data set Defines the QSAR Claim.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Development of an Interpretable QSAR Model for Predicting Coagulation Factor XIIa Inhibitors Using Ensemble Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- MolScope: A Transparent Representation-Aware Workflow Toolkit for Reproducible Medicinal-Chemistry Triage.Molecular informatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has greatly expanded QSAR modeling, but predictive claims still depend on choices that are rarely documented: how chemicals are represented, how end points are defined, and how evaluations are designed. In the era of benchmarks and foundation models, inconsistent standardization, unclear rules for combining measurements, and hidden information leakage routinely inflate reported performance while obscuring weaknesses that matter for real-world applications. We propose data set contracts: executable, auditable documents that explicitly declare chemical processing rules, end point definitions, aggregation logic, data splits, and leakage diagnostics for the intended prediction scenario. These contracts are feasible with current open-source tools and would shift the field from architecture-centric comparisons toward claims that are transparent, reproducible, and trustworthy.
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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.