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4

An equivariant pretrained transformer for unified 3D molecular representation learning

Rui JiaoXiangzhe KongLi ZhangZiyang YuFangyuan Ren8
Nature Communications
2026
2026/2/10
Vol.17 No.1 p.26060
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models in a specific domain, missing the opportunity to leverage cross-domain knowledge. To mitigate this gap, we introduce Equi...
Computational modelsMachine learningMethod development
10.1038/S41467-026-69185-7
ISSN:2041-1723

A method for multiple-sequence-alignment-free protein structure prediction using a protein language model

Xiaomin FangFan WangLihang LiuJingzhou HeDayong Lin11
Nature Machine Intelligence
2023
2023/10/9
Vol.5 No.10 p.1087-1096
Protein structure prediction pipelines based on artificial intelligence, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on multiple sequence alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheles...
Machine learningProtein structure predictions
10.1038/S42256-023-00721-6
ISSN:2522-5839

Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1

Lihang LiuYang LiuXianbin YeShanzhuo ZhangYuxin Li10
Nature Machine Intelligence
2026
2026/7/2
00 p.1-12
Generating large ensembles of candidate conformations is standard for improving biomolecular structure prediction. Yet aimless sampling is inefficient and costly, producing many redundant conformations with limited diversity, particularly for complex multimeric assemblies. Here we present HelixFold-...
Machine learningProtein foldingProtein structure predictions
10.1038/S42256-026-01264-2
ISSN:2522-5839

Atomic context-conditioned protein sequence design using LigandMPNN

Justas DauparasGyu Rie LeeRobert PecoraroLinna AnIvan Anishchenko7
Nature Methods
2025
2025/3/28
Vol.22 No.4 p.717-723
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprotein atoms and molecules. Here we describe a deep-le...
Biophysical chemistryComputational platforms and environmentsProtein designProteins
10.1038/S41592-025-02626-1
ISSN:1548-7091