Evidence map›Paper›PMID 42856349›Full record

ArticlebioRxiv : the preprint server for biology2026

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry.

Zi-Qi Li, Ruijie K Zhang, Pei-Pei Xie, Jacob Kelly, Yu Zhang, Zi-Yang Qin, Theophile Lambert, Orna Mukhopadhyay, Deirdre Hanley, Amy E Danson and 7 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Zi-Qi LiDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0000-0002-1353-3004
Ruijie K ZhangGoogle DeepMind, London, United Kingdom.
Pei-Pei XieDepartment of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.ORCID 0000-0002-7801-5232
Jacob KellyGoogle DeepMind, London, United Kingdom.ORCID 0000-0002-2175-5686
Yu ZhangDepartment of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.ORCID 0000-0001-9412-0246
Zi-Yang QinDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0000-0001-7356-6927
Theophile LambertDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0009-0005-8485-8729
Orna MukhopadhyayDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0009-0009-1253-7722
Deirdre HanleyDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0009-0007-9797-466X
Amy E DansonGoogle DeepMind, London, United Kingdom.ORCID 0000-0003-0869-0459
Josh AbramsonGoogle DeepMind, London, United Kingdom.ORCID 0009-0000-3496-6952
Sukhdeep SinghGoogle DeepMind, London, United Kingdom.
Zachary WuGoogle DeepMind, London, United Kingdom.ORCID 0000-0003-2429-9812
Pushmeet KohliGoogle DeepMind, London, United Kingdom.
Peng LiuDepartment of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.ORCID 0000-0002-8188-632X
Jue WangGoogle DeepMind, London, United Kingdom.ORCID 0000-0002-1139-6640
Frances H ArnoldDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0000-0002-4027-364X

Funding

Computational Models for Reactivity and Selectivity in Transition Metal-Catalyzed Olefin FunctionalizationR35GM128779 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Peng Liu · 2018 to 2026
$2.9M
NIGMS NIH HHS R35 GM128779
6 · The paper itself

Abstract

Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven

Identifiers

PMID42856349
PMCPMC13648690

What OpenQuestion holds

Textmetadata
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.