Evidence map›Paper›PMID 39026784›Full record

ArticlebioRxiv : the preprint server for biology2024

The impact of Library Size and Scale of Testing on Virtual Screening.

Fangyu Liu, Olivier Mailhot, Isabella S Glenn, Seth F Vigneron, Violla Bassim, Xinyu Xu, Karla Fonseca-Valencia, Matthew S Smith, Dmytro S Radchenko, James S Fraser and 3 more

Abstract readPreprint
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

13 authors.

Fangyu LiuDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Olivier MailhotDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Isabella S GlennDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Seth F VigneronDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Violla BassimDept. of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco CA 94143, USA.
Xinyu XuDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Karla Fonseca-ValenciaDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Matthew S SmithDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Dmytro S RadchenkoEnamine Ltd., Kyiv, 02094, Ukraine.
James S FraserDept. of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco CA 94143, USA.
Yurii S MorozEnamine Ltd., Kyiv, 02094, Ukraine.
John J IrwinDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.
Brian K ShoichetDept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA.ORCID 0000-0002-5080-2859

Funding

User Training and OutreachP30GM124169 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI Gregory L Hura · 2017 to 2026
$28.6M
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
A Web-Based Automatic Virtual Screening SystemR01GM071896 · NIGMS · UNIVERSITY OF TORONTO · PI IRWIN, JOHN J. · 2004 to 2024
$6.0M
Eliminating Critical Systematic Errors In Structural Biology With Next-Generation SimulationR01GM124149 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOLTON, JAMES M · 2017 to 2024
$2.5M
Molecular Biophysics Training GrantT32GM149436 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Aashish Manglik · 2023 to 2026
$1.6M
NIGMS NIH HHS P30 GM124169NIGMS NIH HHS R01 GM071896NIGMS NIH HHS R01 GM124149NIGMS NIH HHS R35 GM122481NIGMS NIH HHS T32 GM149436
6 · The paper itself

Abstract

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the

Identifiers

PMID39026784
PMCPMC11257449

What OpenQuestion holds

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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.