Evidence map›Paper›PMID 37483198›Full record

ArticleACS omega2023

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries.

Wen Torng, Ilaria Biancofiore, Sebastian Oehler, Jin Xu, Jessica Xu, Ian Watson, Brenno Masina, Luca Prati, Nicholas Favalli, Gabriele Bassi and 3 more

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

11 citing papers in PubMed, 21 citations in OpenAlex.

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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

13 authors at 3 institutions in 3 countries.

Wen TorngGoogle Research, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States.
Ilaria BiancofioreR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.
Sebastian OehlerR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.ORCID https://orcid.org/0000-0003-2013-7381
Jin XuGoogle Research, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States.
Jessica XuGoogle Research, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States.ORCID https://orcid.org/0000-0003-2587-9587
Ian WatsonGoogle Research, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States.
Brenno MasinaR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.
Luca PratiR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.
Nicholas FavalliR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.
Gabriele BassiR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.ORCID https://orcid.org/0000-0002-9439-8624
Dario NeriR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.
Samuele CazzamalliR&D Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland.ORCID https://orcid.org/0000-0003-0510-5664
Jianwen A FengGoogle Research, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States.
Philochem (Switzerland) · CHGoogle (United States) · USETH Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC

Identifiers

PMID37483198
PMCPMC10357458
OpenAlexW4383314004

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.