Evidence map›Paper›PMID 37614363›Full record

ArticleChem2023

Generative design of

Bo Ni, David L Kaplan, Markus J Buehler

Abstract read
In one paragraph

Article in Chem, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
  2. Review
  3. Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026
    Review
  4. Article
  5. Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  6. Article
  7. Review of diffusion models and its applications in biomedical informatics.BMC medical informatics and decision making · 2025
    Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review on Advancement of AI in Synthetic Biology.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  18. Review
  19. Review
  20. Review
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

3 authors.

Bo NiLaboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.
David L KaplanDepartment of Biomedical Engineering, Tufts University, Medford, MA 02155, USA.
Markus J BuehlerLaboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

Funding

Models to Predict Protein Biomaterial PerformanceU01EB014976 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI BUEHLER, MARKUS J., KAPLAN, DAVID L. · 2012 to 2020
$5.2M
Strain Analysis Software for Open ScienceR01AR077793 · NIAMS · WASHINGTON UNIVERSITY · PI Guy M Genin, Stavros Thomopoulos · 2020 to 2026
$5.2M
NIAMS NIH HHS R01 AR077793NIBIB NIH HHS U01 EB014976
6 · The paper itself

Abstract

We report two generative deep learning models that predict amino acid sequences and 3D protein structures based on secondary structure design objectives via either overall content or per-residue structure. Both models are robust regarding imperfect inputs and offer

Indexed as

attention modelsdeep learningdiffusion modelProteins designtransformer

Identifiers

PMID37614363
PMCPMC10443900

What OpenQuestion holds

Textmetadata
LicenceTDM
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.