ConforNets: Latents-Based Conformational Control in OpenFold3.

Minji Lee, Colin Kalicki, Minkyu Jeon, Aymen Qabel, Alisia Fadini, Mohammed AlQuraishi

Abstract readPreprint

In one paragraph

Article in ArXiv, 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

6 authors.

Minji LeeDepartment of Computer Science, Columbia University, NY, USA.

Colin KalickiDepartment of Systems Biology, Columbia University, NY, USA.

Minkyu JeonDepartment of Computer Science, Princeton University, Princeton, NJ, USA.

Aymen QabelDepartment of Computer Science, Columbia University, NY, USA.

Alisia FadiniDepartment of Systems Biology, Columbia University, NY, USA.

Mohammed AlQuraishiDepartment of Computer Science, Columbia University, NY, USA.

Funding

Machine learning of biomolecular interactions and the human signaling networks they compriseR35GM150546 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Mohammed Nazar AlQuraishi · 2023 to 2026

$1.6M

Fast and slow prediction of stable and transient protein-protein interactionsR01LM014674 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI ALQURAISHI, MOHAMMED NAZAR · 2025 to 2025

$1.4M

NIGMS NIH HHS R35 GM150546NLM NIH HHS R01 LM014674

6 · The paper itself

Abstract

Models from the AlphaFold (AF) family reliably predict one dominant conformation for most well-ordered proteins but struggle to capture biologically relevant alternate states. Several efforts have focused on eliciting greater conformational variability through

Identifiers

PMID42078495

PMCPMC13131846

What OpenQuestion holds

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LicenceCC BY

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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.