Evidence map›Paper›PMID 42410651›Full record

ArticleJournal of cheminformatics2026

Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.

Ju Hyung Lee, Seongik Choi, Utku Ozbulak, Joris Vankerschaver, Wesley De Neve

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cheminformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ju Hyung Lee *Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea. juhyung.lee@ghent.ac.kr.ORCID https://orcid.org/0009-0001-5191-7420
Seongik Choi *Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea. seongik.choi@ghent.ac.kr.
Utku OzbulakCenter for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea.
Joris VankerschaverCenter for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea.
Wesley De NeveCenter for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative prediction of inhibitor potency can accelerate early-stage drug discovery. Recently, data-driven approaches have gained widespread interest in drug discovery, as evidenced by a growing number of benchmarking challenges and open competitions. In this context, we developed a machine learning-based methodology that can find the most effective way of predicting IC

Indexed as

ASK1ChemBERTaIC50 RegressionSMILES

Identifiers

PMID42410651
PMCPMC13625499

What Socratic holds

Textmetadata
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 Socratic graph.