Evidence map›Paper›PMID 36179327›Full record

ArticleChemical research in toxicology2022

Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment.

Alexander K Hurben, Luke Erber

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemical research in toxicology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Alexander K HurbenDepartment of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States.ORCID 0000-0002-4016-1103
Luke ErberDepartment of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States.

Funding

Training Research Educators in Minnesota (TREM) whilst increasing diversityK12GM119955 · NIGMS · UNIVERSITY OF MINNESOTA · PI David Irwin Greenstein, Julia Willett · 2017 to 2026
$6.5M
Training the Next Generation of Chemical BiologistsT32GM132029 · NIGMS · UNIVERSITY OF MINNESOTA · PI Erin Elizabeth Carlson, William Charles Krause Pomerantz · 2019 to 2026
$2.7M
Chemical Toxicology Division SymposiumR13ES033526 · NIEHS · NORTHEASTERN UNIVERSITY · PI BEUNING, PENNY J · 2021 to 2021
$8k
Division of Chemical Toxicology Symposia at the 264th National ACS MeetingR13ES034642 · NIEHS · BAYLOR UNIVERSITY · PI TRAKSELIS, MICHAEL A · 2022 to 2022
$8k
NIEHS NIH HHS R13 ES033526NIEHS NIH HHS R13 ES034642NIGMS NIH HHS K12 GM119955NIGMS NIH HHS T32 GM132029
6 · The paper itself

Abstract

Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied across various facets of drug design, development, and safety assessment.

Indexed as

Artificial IntelligenceDrug DiscoveryDrug Design

Identifiers

PMID36179327
PMCPMC9886504

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.