Evidence map›Paper›PMID 40667369›Full record

ArticlebioRxiv : the preprint server for biology2025

Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction.

Saro Passaro, Gabriele Corso, Jeremy Wohlwend, Mateo Reveiz, Stephan Thaler, Vignesh Ram Somnath, Noah Getz, Tally Portnoi, Julien Roy, Hannes Stark and 4 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Comparing YAPBiomolecules · 2026
    Article
  2. Novel Functionalized Pyrrolopyridines to Target Brk.Molecules (Basel, Switzerland) · 2026
    Article
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  4. Review
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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

14 authors.

Saro PassaroMIT CSAIL.
Gabriele CorsoMIT CSAIL.ORCID 0000-0002-1963-8755
Jeremy WohlwendMIT CSAIL.ORCID 0000-0002-1993-2380
Mateo ReveizMIT CSAIL.ORCID 0000-0002-5680-4150
Stephan ThalerValence Labs.ORCID 0000-0001-5383-1615
Vignesh Ram SomnathETH Zurich.
Noah GetzMIT CSAIL.
Tally PortnoiMIT CSAIL.ORCID 0000-0002-8543-7855
Julien RoyValence Labs.
Hannes StarkMIT CSAIL.ORCID 0000-0002-4463-326X
David Kwabi-AddoMIT CSAIL.
Dominique BeainiValence Labs.ORCID 0000-0002-4613-9388
Tommi JaakkolaMIT CSAIL.ORCID 0000-0002-2199-0379
Regina BarzilayMIT CSAIL.

Funding

Graduate Training in Computational and Systems BiologyT32GM087237 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BURGE, CHRISTOPHER B · 2009 to 2023
$4.6M
MATCHMAKERS - Creating and training AI tools for TCR binding prediction and designOT2CA297463 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Michael Birnbaum · 2024 to 2026
$2.6M
NCI NIH HHS OT2 CA297463NIGMS NIH HHS T32 GM087237
6 · The paper itself

Abstract

Accurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule-protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000× more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code under a permissive open license, providing a robust and extensible foundation for both academic and industrial research.

Identifiers

PMID40667369
PMCPMC12262699

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.