Evidence map›Paper›PMID 32110289›Full record

ArticleChemical science2019

Delfos: deep learning model for prediction of solvation free energies in generic organic solvents.

Hyuntae Lim, YounJoon Jung

Abstract read
In one paragraph

Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. A Solvent Selection Framework for Porous Organic Polymers.Journal of chemical information and modeling · 2025
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  3. Review
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  5. Article
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  7. Article
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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

2 authors.

Hyuntae LimDepartment of Chemistry , Seoul National University , Seoul 08826 , Korea . Email: ht0620@snu.ac.kr ; Email: yjjung@snu.ac.kr.ORCID 0000-0003-0342-3482
YounJoon JungDepartment of Chemistry , Seoul National University , Seoul 08826 , Korea . Email: ht0620@snu.ac.kr ; Email: yjjung@snu.ac.kr.ORCID 0000-0002-9464-9999

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediction of aqueous solubilities or hydration free energies is an extensively studied area in machine learning applications in chemistry since water is the sole solvent in the living system. However, for non-aqueous solutions, few machine learning studies have been undertaken so far despite the fact that the solvation mechanism plays an important role in various chemical reactions. Here, we introduce

Identifiers

PMID32110289
PMCPMC7017869

What OpenQuestion holds

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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.