Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes.

Pengfei Zhang, Sonal Sujit Prabhu, Gloria Grama, Seojin Bang, Heewook Lee

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Iterative attack-and-defend framework for improving TCR-epitope binding prediction models.Bioinformatics (Oxford, England) · 2025
Article

4 · The record

Corrections and comments

Update of
Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes.2025

5 · Who and what money

Authors and funding

5 authors.

Pengfei ZhangSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281.

Sonal Sujit PrabhuSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281.

Gloria GramaSchool of Life Sciences, Arizona State University, Tempe, AZ 85281.

Seojin BangGoogle DeepMind, Mountain View, CA 94043.

Heewook LeeSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281.

Funding

Machine Learning Models for Studying Protein Interactions in the Context of Immune ReceptorsR35GM155417 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI Heewook Lee · 2024 to 2026

$1.1M

NIGMS NIH HHS R35 GM155417

6 · The paper itself

Abstract

Computational design of T cell receptors (TCRs) that bind to epitopes holds the potential to revolutionize targeted immunotherapy. However, computational design of TCRs for novel epitopes is challenging due to the scarcity of training data, and the absence of known cognate TCRs for novel epitopes. In this study, we aim to generate high-quality cognate TCRs particularly for

Identifiers

PMID42038995

PMCPMC13103298

What OpenQuestion holds

Textmetadata

LicenceTDM

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.