ArticleJournal of computer-aided molecular design2021
Multiple linear regression models for predicting the n‑octanol/water partition coefficients in the SAMPL7 blind challenge.
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 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Expanded ensemble predictions of toluene-water partition coefficients in the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2025Article
- Fragment contribution models for predicting skin permeability using HuskinDB.Scientific data · 2023Article
- Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.Journal of computer-aided molecular design · 2021Article
- Evaluation of log P, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log P
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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.