Evidence map›Paper›PMID 34169394›Full record

ArticleJournal of computer-aided molecular design2021

Evaluation of log P, pK

Teresa Danielle Bergazin, Nicolas Tielker, Yingying Zhang, Junjun Mao, M R Gunner, Karol Francisco, Carlo Ballatore, Stefan M Kast, David L Mobley

Abstract read
In one paragraph

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 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

  1. Article
  2. Review
  3. Unifying pJournal of chemical information and modeling · 2026
    Article
  4. Article
  5. Benchmarking pJournal of chemical information and modeling · 2026
    Article
  6. Article
  7. Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  8. Article
  9. Review
  10. Review
  11. From NMR to AI: FusingJournal of chemical information and modeling · 2025
    Article
  12. Review
  13. Predicting pRSC advances · 2025
    Article
  14. Article
  15. Article
  16. Interpretable Deep-Learning pJournal of chemical information and modeling · 2025
    Article
  17. Article
  18. Ethanolic Extract ofPharmaceutics · 2024
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Teresa Danielle BergazinDepartment of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.ORCID https://orcid.org/0000-0002-0573-6178
Nicolas TielkerPhysikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany.ORCID https://orcid.org/0000-0003-0974-8739
Yingying ZhangDepartment of Physics, The Graduate Center, City University of New York, New York, 10016, USA.ORCID https://orcid.org/0000-0003-3769-079X
Junjun MaoDepartment of Physics, City College of New York, New York, 10031, USA.ORCID https://orcid.org/0000-0002-3106-3018
M R GunnerDepartment of Physics, The Graduate Center, City University of New York, New York, 10016, USA.ORCID https://orcid.org/0000-0003-1120-5776
Karol FranciscoSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Ja Jolla, CA, 92093-0756, USA.ORCID https://orcid.org/0000-0001-9742-8801
Carlo BallatoreSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Ja Jolla, CA, 92093-0756, USA.ORCID https://orcid.org/0000-0002-2718-3850
Stefan M KastPhysikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany.ORCID https://orcid.org/0000-0001-7346-7064
David L MobleyDepartment of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA. dmobley@uci.edu.ORCID http://orcid.org/0000-0002-1083-5533

Funding

Advancing predictive physical modeling through focused development of model systems to drive new modeling innovationsR01GM124270 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI MOBLEY, DAVID LOWELL · 2018 to 2021
$1.5M
NIGMS NIH HHS R01 GM124270
6 · The paper itself

Abstract

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK

Indexed as

Computational BiologyComputer SimulationDrug DesignEntropyHumansLigandsModels, ChemicalModels, StatisticalOctanolsQuantum TheorySoftwareSolubilitySolventsSulfonamidesThermodynamicsWaterLigandsOctanolsSolventsSulfonamidesWaterFree energy calculationslog PpK aSAMPL

Identifiers

PMID34169394
PMCPMC8224998

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.