Evidence map›Paper›PMID 39071262›Full record

ArticlebioRxiv : the preprint server for biology2024

Identifying Artifacts from Large Library Docking.

Yujin Wu, Fangyu Liu, Isabella Glenn, Karla Fonseca-Valencia, Lu Paris, Yuyue Xiong, Steven V Jerome, Charles L Brooks, Brian K Shoichet

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

9 authors.

Yujin WuDepartment of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States.
Fangyu LiuDepartment of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States.
Isabella GlennDepartment of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States.
Karla Fonseca-ValenciaDepartment of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States.
Lu ParisDepartment of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States.
Yuyue XiongSchrödinger, Inc., 9868 Scranton Road, San Diego, California 92121, United States.
Steven V JeromeSchrödinger, Inc., 1540 Broadway, New York, New York, 10036, United States.
Charles L BrooksBiophysics Program, University of Michigan, Ann Arbor, Michigan 48109, United States.
Brian K ShoichetDepartment of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States.

Funding

Development and Testing of New Computational Methods for Ligand Discovery and MechanismR35GM122481 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Brian K Shoichet · 2017 to 2026
$8.5M
NIGMS NIH HHS R35 GM122481
6 · The paper itself

Abstract

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown, the very top of the hit-lists can become populated with artifacts that cheat our scoring functions. Though these cheating molecules are rare, they become ever-more dominant with library growth. Here, we investigate rescoring top-ranked molecules from docking screens with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation (AB-FEP) as cross-filters. In retrospective studies, this approach deprioritized high-ranking non-binders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against results from large library docking AmpC

Identifiers

PMID39071262
PMCPMC11275789

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.