Evidence map›Paper›PMID 41862506›Full record

ArticleScientific data2026

Dataset of solubility values for organic compounds in binary mixtures of solvents at various temperatures.

Dmitry Malikov, Lev Krasnov, Marina Kiseleva, Elizaveta Meshcheriakova, Fedor Kuznetsov, Vladimir Elistratov, Matvei Vasiyarov, Sergei Tatarin, Stanislav Bezzubov

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In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Dmitry Malikov *N.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Lev Krasnov *N.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia. lewa.krasnovs@gmail.com.
Marina KiselevaN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Elizaveta MeshcheriakovaN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Fedor KuznetsovN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Vladimir ElistratovN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Matvei VasiyarovN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Sergei TatarinN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia.
Stanislav BezzubovN.S. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991 Leninskii pr. 31, Moscow, Russia. bezzubov@igic.ras.ru.

Funding

Program for Fundamental Research of the N.S. Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences 1021071612866-5-1.4.7
6 · The paper itself

Abstract

Solubility is a crucial property of organic compounds, impacting their potential applications in synthetic chemistry, materials science and drug design. Moreover, in technological processes mixtures of solvents are often utilized, making the solubility assessment more complicated. Predicting solubility values in mixtures of solvents from a molecular structure can help to address this issue, although a large and diverse dataset is needed to effectively pursue data-driven studies. In this research, we present a dataset containing 175166 experimental solubility values within the temperature range from 252 to 383 K for 810 organic compounds possessing 3001 unique solute-binary solvent systems and 750 unique binary solvent mixtures extracted from 1115 peer-reviewed articles. The solubility data and molecular structures of solutes and solvents are translated to a unified machine-readable format, facilitating data analysis and machine learning model development. An interactive online tool for visualization and navigation through the data has also been developed. This dataset can serve as a comprehensive benchmark for predicting solubility in mixtures of solvents.

Identifiers

PMID41862506
PMCPMC13168504

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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.