School of EECS；Dept. of Biochemistry \& Biophysics , Oregon State University , USA；Center for RNA Biology；Computational Biology , University of Rochester Medical Center , USA
Milan Gautam, Ning Dai, Tianshuo Zhou, Bowen Xie, David Mathews, Liang Huang
RNA design, the task of finding a sequence that folds into a target secondary structure, has broad biological and biomedical impact but remains computationally challenging due to the exponentially large sequence space and exponentially many competing folds. Traditional approaches treat it as an optimization problem, relying on per-instance heuristics or constraint-based search. We instead reframe RNA design as conditional sequence generation and introduce a reusable neural approximator, instantiated as an autoregressive language model (LM), that maps target structures directly to sequences. We first train our model in a supervised setting on random-induced structure-sequence pairs, and then use reinforcement learning (RL) to optimize end-to-end metrics. We also propose methods to select a small subset for RL that greatly improves RL efficiency and quality. Across four datasets, our approach outperforms state-of-the-art systems on key metrics such as Boltzmann probability while being 1.7x faster, establishing conditional LM generation as a scalable, task-agnostic alternative to per-instance optimization for RNA design. Our code and data are available at https://github.com/KuNyaa/RNA-Design-LM.
LoRA-BERT: a Natural Language Processing Model for Robust and Accurate Prediction of long non-coding RNAs
Texas A&M University（德克萨斯农工大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；The University of Missouri（密苏里大学）；University of Missouri School of Medicine（密苏里大学医学院）
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory（深圳湾实验室系统与物理生物学研究所）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Ribopeutic Inc（Ribopeutic公司）
Meta-Designing Quantum Experiments with Language Models
Max Planck Institute for the Science of Light（马克斯·普朗克光科学研究所）；University of Tuebingen（图宾根大学）；Stanford University（斯坦福大学）；University of Toronto, Vector Institute（多伦多大学、向量研究所）
Designing Protein Structures from Natural Language
University of Washington（华盛顿大学）；National University of Singapore（新加坡国立大学）；Allen Institute for AI (Ai2)（艾伦人工智能研究所）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
Accurate RNA 3D structure prediction using a language model-based deep learning approach
The Chinese University of Hong Kong（香港中文大学）；Zelixir Biotech（泽利昔生物科技公司）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；Wyss Institute for Biologically Inspired Engineering（威斯生物启发工程研究所）；Harvard University（哈佛大学）；Harvard Medical School（哈佛医学院）；Arizona State University（亚利桑那州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）；Synthetic Biology Center（合成生物学中心）；OneAIM Ltd.（OneAIM有限公司）
gRNAde: Geometric Deep Learning for 3D RNA inverse design
University of Cambridge（剑桥大学）；Prescient Design, Genentech, Roche（罗氏基因泰克Prescient Design部门）；EPFL（洛桑联邦理工学院）；University of Missouri（密苏里大学）；National University of Singapore（新加坡国立大学）
gRNAde利用多状态图神经网络和自回归解码，在3D RNA骨架上进行逆向设计，显著提升天然序列恢复率（56% vs 45%）和假结设计成功率（50% vs 35%）。
