ArticleJournal of computer-aided molecular design2021
Automated high throughput pK
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Unifying pJournal of chemical information and modeling · 2026Article
- Benchmarking pJournal of chemical information and modeling · 2026Article
- Development of a pKa predictor (pKaLearn) by leveraging teaching experience to improve machine learning.Communications chemistry · 2026Article
- Expanded ensemble predictions of toluene-water partition coefficients in the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2025Article
- Interpretable Deep-Learning pJournal of chemical information and modeling · 2025Article
- COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge.Journal of computer-aided molecular design · 2023Article
- Automated high throughput pKJournal of computer-aided molecular design · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pK
Indexed as
Identifiers
What OpenQuestion holds
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.