Evidence map›Paper›PMID 41843673›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Dual-encoder contrastive learning accelerates enzyme discovery.

Jason W Rocks, Dat P Truong, Dmitrij Rappoport, Samuel Maddrell-Mander, Daniel A Martin-Alarcon, Toni M Lee, Steven Crossan, Joshua E Goldford

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026
    Article
  2. 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

8 authors.

Jason W RocksDayhoff Labs, Inc., Cambridge, MA 02140.ORCID 0000-0002-0285-0119
Dat P TruongDayhoff Labs, Inc., Cambridge, MA 02140.ORCID 0000-0003-1248-1369
Dmitrij RappoportDayhoff Labs, Inc., Cambridge, MA 02140.
Samuel Maddrell-ManderDayhoff Labs, Inc., Cambridge, MA 02140.
Daniel A Martin-AlarconDayhoff Labs, Inc., Cambridge, MA 02140.ORCID 0000-0003-0547-8129
Toni M LeeDayhoff Labs, Inc., Cambridge, MA 02140.ORCID 0009-0007-4624-5889
Steven CrossanDayhoff Labs, Inc., Cambridge, MA 02140.
Joshua E GoldfordDayhoff Labs, Inc., Cambridge, MA 02140.ORCID 0000-0001-7315-8018

Funding

U.S. Department of Energy (DOE) DE-SC0025760
6 · The paper itself

Abstract

The ability to engineer enzymes for desired reactions is a cornerstone of modern biotechnology, yet identifying suitable starting proteins remains a critical bottleneck. Although contrastive learning offers a compelling computational approach for enzyme discovery, these models have yet to be implemented at scale or proven effective in real-world experimental settings. Here, we present

Indexed as

Computational BiologyDeep LearningEnzymesProtein EngineeringEnzymesAIdeep learningenzyme discoveryenzymology

Identifiers

PMID41843673
PMCPMC13012038

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.