Evidence map›Paper›PMID 42039485›Full record

ArticlebioRxiv : the preprint server for biology2026

A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins.

Connor V Galvin, Amy B Guo, Maple N Chen, Isabella L Alfonso, Dominic Grisingher, Simon Kretschmer, Divya Kranthi, Mark J S Kelly, Tanja Kortemme

Abstract readPreprint
PubMed Full text at PMC Publisher
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

9 authors.

Connor V GalvinDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.
Amy B GuoDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.ORCID 0000-0003-4775-9498
Maple N ChenDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.
Isabella L AlfonsoDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.
Dominic GrisingherDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.
Simon KretschmerDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.
Divya KranthiBiophysics Graduate Program, University of California, San Francisco; San Francisco, CA 94143, USA.
Mark J S KellyDepartment of Pharmaceutical Chemistry, University of California, San Francisco; San Francisco, CA 94143, USA.
Tanja KortemmeDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.

Funding

Equipment Supplement - Computational design of proteins and protein functionsR35GM145236 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORTEMME, TANJA · 2022 to 2025
$2.4M
A 600 MHz NMR console and cryoprobe for studies of macromolecules, drug discovery and cancer biomarkersS10OD023455 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GROSS, JOHN D · 2018 to 2018
$820k
NIGMS NIH HHS R35 GM145236NIH HHS S10 OD023455
6 · The paper itself

Abstract

Engineering small-molecule binding proteins de novo remains a significant challenge as even advanced generative models struggle to model the atom-level details of protein-ligand interactions with sufficient accuracy. Higher experimental success rates have resulted from methods that explicitly scaffold predefined binding interactions into helical bundles. Here we introduce a scaffolding strategy that generalizes to alpha-beta architectures. By screening thousands of combinatorially assembled protein-ligand interactions against diverse de novo backbones with finely varied pocket geometries, the protocol allows for high-fidelity accommodation of target interaction geometries. Our protocol then integrates physics-based and deep learning methods for optimization of interfacial interactions and sequence-structure compatibility, considerably improving

Identifiers

PMID42039485
PMCPMC13104977

What OpenQuestion holds

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LicenceCC BY-NC
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.