Evidence map›Paper›PMID 36873901›Full record

ArticlePatterns (New York, N.Y.)2023

SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.

Yongheng Wang, Weidi Zhang, Hoyin Yip, Chuyuan Qu, Hongru Hu, Xiaotie Chen, Teresa Lee, Xi Yang, Bingjun Yang, Priyadarsini Kumar and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025
    Review
  3. 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

15 authors at 4 institutions in 1 country.

Yongheng WangDepartment of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA.
Weidi ZhangCenter for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.
Hoyin YipCenter for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.
Chuyuan QuMicrosoft, Redmond, WA 98052, USA.
Hongru HuIntegrative Genetics and Genomics, University of California, Davis, Davis, CA 95616, USA.
Xiaotie ChenDepartment of Mathematics, University of California, Davis, Davis, CA 95616, USA.
Teresa LeeDepartment of Biomedical Engineering, Yale University, New Haven, CT 06511, USA.
Xi YangIntel, Santa Clara, CA 95054, USA.
Bingjun YangCenter for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.
Priyadarsini KumarCenter for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.
Su Yeon LeeCenter for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.
Javier J CasimiroCenter for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.
Jiawei ZhangDepartment of Computer Science, IFM Lab, University of California, Davis, Davis, CA 95616, USA.
Aijun WangDepartment of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA.
Kit S LamDepartment of Biochemistry and Molecular Medicine, UC Davis NCI-designated Comprehensive Cancer Center, University of California, Davis, Sacramento, CA 95817, USA.
University of California, Davis · USIntel (United States) · USMicrosoft (United States) · USYale University · US

Funding

HER2-targeting transformable nanotherapeutic platform against HER2+ cancersR01CA247685 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI LAM, KIT S · 2020 to 2024
$3.1M
NCI NIH HHS R01 CA247685
6 · The paper itself

Abstract

As a measure of cytotoxic potency, half-maximal inhibitory concentration (IC50) is the concentration at which a drug exerts half of its maximal inhibitory effect against target cells. It can be determined by various methods that require applying additional reagents or lysing the cells. Here, we describe a label-free Sobel-edge-based method, which we name SIC50, for the evaluation of IC50. SIC50 classifies preprocessed phase-contrast images with a state-of-the-art vision transformer and allows for the continuous assessment of IC50 in a faster and more cost-efficient manner. We have validated this method using four drugs and 1,536-well plates and also built a web application. We anticipate that this method will assist in the high-throughput screening of chemical libraries (e.g., small-molecule drugs, small interfering RNA [siRNA], and microRNA and drug discovery).

Indexed as

cancer drug potencyconvolution neural networkvision transformer

Identifiers

PMID36873901
PMCPMC9982297
OpenAlexW4319076995

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.