Evidence map›Paper›PMID 40646169›Full record

ArticleNature biotechnology2026

Transitive prediction of small-molecule function through alignment of high-content screening resources.

Feng Bao, Li Li, Heinz Hammerlindl, Susan Q Shen, Sabrina Hammerlindl, Steven J Altschuler, Lani F Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

7 authors.

Feng BaoDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-5721-9551
Li LiDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
Heinz HammerlindlDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0001-5119-9791
Susan Q ShenDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
Sabrina HammerlindlDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
Steven J AltschulerDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA. steven.altschuler@ucsf.edu.ORCID http://orcid.org/0000-0001-9142-0796
Lani F WuDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA. lani.wu@ucsf.edu.ORCID http://orcid.org/0000-0002-0052-7537

Funding

TRAINING THE NEXT GENERATION OF MENTAL HEALTH RESEARCHERSR25MH060482 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DANIEL H MATHALON, Susan M. Voglmaier · 2000 to 2026
$5.2M
Maximizing the predictive power of high-throughput, microscopy-based phenotypic screensR01CA184984 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WU, LANI F · 2014 to 2025
$4.7M
Promoting Multidisciplinary Clinical and Translational Science in Aging by Resident InvestigatorsR38AG070171 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Alison Huang, Ashwin Ajit Kotwal · 2021 to 2026
$1.8M
Elucidate the landscape of druggable targets for eliminating cancer persister cellsR01CA300245 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI STEVEN J ALTSCHULER · 2025 to 2026
$1.4M
NCI NIH HHS R01 CA184984NCI NIH HHS R01 CA300245NIA NIH HHS R38 AG070171NIMH NIH HHS R25 MH060482
6 · The paper itself

Abstract

High-content image-based phenotypic screens (HCSs) provide a scalable approach to characterize biological functions of compounds. The widespread adoption of HCS has led to a growing body of available profile datasets. However, study-specific experimental and computational choices lead to profile datasets that cannot be directly combined. A critical, long-standing challenge is how to integrate these rich but currently isolated HCS dataset resources. Here we introduce a contrastive, deep-learning framework that leverages sparse sets of overlapping profiles as fiducials to align heterogeneous HCS profile datasets in a shared latent space. We demonstrate that this alignment facilitates accurate 'transitive' predictions, whereby the function of an uncharacterized compound screened in one dataset can be predicted through comparison with characterized compounds already profiled in other datasets. In silico alignment of HCS resources provides a path to unify fast-growing HCS resources and accelerate early drug discovery efforts.

Indexed as

Drug DiscoveryHigh-Throughput Screening AssaysSmall Molecule LibrariesComputer SimulationDeep LearningHumansSmall Molecule Libraries

Identifiers

PMID40646169
PMCPMC12846546

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.