Evidence map›Paper›PMID 36696568›Full record

ArticleJournal of chemical information and modeling2023

Preventing AI From Creating Biochemical Threats.

Fabio Urbina, Filippa Lentzos, Cédric Invernizzi, Sean Ekins

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

4 authors.

Fabio UrbinaCollaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
Filippa LentzosDepartment of War Studies and Department of Global Health & Social Medicine, King's College London, Strand, London WC2R 2LS, United Kingdom.
Cédric InvernizziSpiez Laboratory, Federal Department of Defence, Civil Protection and Sports, Austrasse, 3700 Spiez, Switzerland.ORCID 0000-0002-9807-5195
Sean EkinsCollaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.ORCID 0000-0002-5691-5790

Funding

Centralized assay datasets for modelling support of small drug discovery organizationsR44GM122196 · NIGMS · COLLABORATIONS PHARMACEUTICALS, INC. · PI EKINS, SEAN · 2018 to 2022
$3.3M
MegaTox for analyzing and visualizing data across different screening systemsR43ES031038 · NIEHS · COLLABORATIONS PHARMACEUTICALS, INC. · PI EKINS, SEAN · 2019 to 2020
$349k
Machine learning approaches to predict Acetylcholinesterase inhibitionR43ES033855 · NIEHS · COLLABORATIONS PHARMACEUTICALS, INC. · PI EKINS, SEAN · 2022 to 2022
$256k
Centralized assay datasets for modelling support of small drug discovery organizationsR43GM122196 · NIGMS · COLLABORATIONS PHARMACEUTICALS, INC. · PI EKINS, SEAN · 2017 to 2017
$150k
NIEHS NIH HHS R43 ES031038NIEHS NIH HHS R43 ES033855NIGMS NIH HHS R43 GM122196NIGMS NIH HHS R44 GM122196
6 · The paper itself

Abstract

We have previously applied our machine learning models for bioactivity and toxicity along with a generative algorithm to develop VX and tens of thousands of analogues. The publication brought attention to the ease of designing chemical warfare agents. In this Viewpoint, we discuss 10 recommendations to prevent future biochemical threats.

Indexed as

Chemical Warfare AgentsOrganothiophosphorus CompoundsAlgorithmsMachine LearningChemical Warfare AgentsOrganothiophosphorus Compounds

Identifiers

PMID36696568
PMCPMC10099773

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.