Evidence map›Paper›PMID 42057571›Full record

ArticleMolecular informatics2026

ChemBang: Expanding the Chemical Space Around Small Molecules.

Diana Montes-Grajales, Luca Menestrina, Ricard Garcia-Serna, Jordi Mestres

Abstract read
In one paragraph

Article in Molecular informatics, 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

4 authors.

Diana Montes-GrajalesChemotargets SL, Parc Cientific de Barcelona, Barcelona, Spain.ORCID https://orcid.org/0000-0002-1317-3424
Luca MenestrinaChemotargets SL, Parc Cientific de Barcelona, Barcelona, Spain.
Ricard Garcia-SernaChemotargets SL, Parc Cientific de Barcelona, Barcelona, Spain.
Jordi MestresChemotargets SL, Parc Cientific de Barcelona, Barcelona, Spain.ORCID https://orcid.org/0000-0002-5202-4501

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficient exploration of chemical space is an essential component of modern generative drug design. Herein, we introduce ChemBang, a computational engine that grows small molecules based on chemical transformations extracted by matched molecular pair analysis of all structures available in catalogues of synthesized molecules. Each chemical transformation is mapped onto its associated atomic environment defined as the substructure within a three-atom radius from the transformation site. Unsupervised chemical evolution is then performed in cycles by systematically applying chemical transformations to all exposed atomic environments present in a seed structure. Multiple physicochemical properties and substructural alerts are incorporated to effectively guide the generation of drug-like synthetically accessible molecules. As a use case, the generation of the Erdafitinib structure from any of its three ring systems (pyrazole, benzene and quinoxaline), and the evolution of the property distributions from all molecules generated in each cycle, are discussed in detail. The ability to explore the chemical space of pharmaceutical relevance is shown by successfully generating the exact chemical structure of 95.3% of all 2,809 small-molecule ATC drugs from their constituting fragments.

Indexed as

Drug DesignSmall Molecule LibrariesMolecular StructurePyrazolesPyrazolesSmall Molecule Librarieschemical space explorationcomputational chemistryin‐silico library designlead optimizationmatched molecular pairmolecular generatorscaffold decoration

Identifiers

PMID42057571
PMCPMC13129508

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.