Evidence map›Paper›PMID 42605276›Full record

ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2026

UniPocket: Unified Ligand and Cryptic Pocket Prediction from Protein Language Model Embeddings.

Lening Zhao, Tianhua Zhai, Li Shen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 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

3 authors.

Lening ZhaoUniversity of Pennsylvainia Philadelphia, Pennsylvania, USA.
Tianhua ZhaiUniversity of Pennsylvainia Philadelphia, USA.
Li ShenUniversity of Pennsylvainia Philadelphia, Pennsylvania, USA.

Funding

Peripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Minerva Maria Carrasquillo · 2023 to 2026
$42.0M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Artificial Intelligence Strategies for Alzheimer's Disease ResearchU01AG066833 · NIA · CEDARS-SINAI MEDICAL CENTER · PI MOORE, JASON H., RITCHIE, MARYLYN D · 2022 to 2025
$6.7M
Translational big data analytic approaches to advance drug repurposing for Alzheimer's diseaseR01AG071470 · NIA · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON, NING, XIA · 2021 to 2025
$3.8M
NIA NIH HHS R01 AG071470NIA NIH HHS U01 AG066833NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG074879
6 · The paper itself

Abstract

Identifying druggable pockets in proteins is central to structure-based drug discovery, yet conventional ligand-binding-site prediction and cryptic-pocket prediction are typically treated as separate tasks requiring distinct tools, preprocessing pipelines, and evaluation protocols. This separation is limiting because the two problems share a common biological foundation: co-evolutionary patterns encoded in protein sequences carry implicit signals about both conventional and cryptic binding sites. We present

Indexed as

Cryptic pocketLigand bindingMultitask learningPocket predictionProtein language modelUnified prediction

Identifiers

PMID42605276
PMCPMC13477581

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.