Evidence map›Paper›PMID 42101975›Full record

ArticleJournal of medicinal chemistry2026

The Selectivity Implications of Docking Libraries with Greater and Lesser Similarities to Bio-like Molecules.

Brendan W Hall, Kensuke Sakamoto, Xi-Ping Huang, John J Irwin, Brian K Shoichet, Bryan L Roth

Abstract read
In one paragraph

Article in Journal of medicinal chemistry, 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

5 · Who and what money

Authors and funding

6 authors.

Brendan W HallDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States.ORCID 0000-0003-1616-1303
Kensuke SakamotoDepartment of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina 27599, United States.ORCID 0000-0003-4488-1244
Xi-Ping HuangDepartment of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina 27599, United States.
John J IrwinDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States.
Brian K ShoichetDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States.ORCID 0000-0002-6098-7367
Bryan L RothDepartment of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina 27599, United States.ORCID 0000-0002-0561-6520

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As make-on-demand libraries have expanded into the billions, their similarity to "bio-like" molecules has rapidly diminished. Nevertheless, docking these ultralarge libraries has found actives with high hit rates and affinities. Plausibly, the divergence from bio-like may improve hit selectivity; conversely, if hit rates on target are divorced from bio-like similarity, off-target selectivity may be as well. Here, we test whether docking actives from ultralarge libraries are more selective than those from more bio-like "in-stock" libraries in an in-depth study against a single target, the 5-HT

Indexed as

Molecular Docking SimulationReceptor, Serotonin, 5-HT2ASerotonin 5-HT2 Receptor AgonistsSmall Molecule LibrariesHumansLigandsStructure-Activity RelationshipLigandsReceptor, Serotonin, 5-HT2ASerotonin 5-HT2 Receptor AgonistsSmall Molecule Libraries

Identifiers

PMID42101975
PMCPMC13224090

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.