The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds. Bayesian Optimization is a principled method for tackling this problem. However, the astronomically large state space of biological sequences renders brute-force iterating over all possible sequences infeasible. In this paper, we propose MetaRLBO where we train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection via Bayesian Optimization. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data acquired in the previous rounds. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.
Inverse designing metamaterials with programmable nonlinear functional responses in graph space
University of California Berkeley（加州大学伯克利分校）；University of California Los Angeles（加州大学洛杉矶分校）；Penn State University（宾州州立大学）；CNRS（法国国家科学研究中心）；Institut Jean Lamour（让·拉穆尔研究所）
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Stanford University（斯坦福大学）；Arc Institute（Arc研究所）；Gladstone-UCSF Institute of Genomic Immunology（Gladstone-加州大学旧金山分校基因组免疫学研究所）；University of California, San Francisco（加州大学旧金山分校）；UCSF Helen Diller Family Comprehensive Cancer Center（加州大学旧金山分校海伦·迪勒家族综合癌症中心）
seqme: a Python library for evaluating biological sequence design
Institute of AI for Health, Helmholtz Munich（亥姆霍兹慕尼黑人工智能健康研究所）；Technical University of Munich（慕尼黑工业大学）；University of Warsaw（华沙大学）；Warsaw University of Technology（华沙理工大学）
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）
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Department of Biomedical Engineering, Duke University（杜克大学生物医学工程系）；Department of Computer Science, Fudan University（复旦大学计算机科学系）；Center of Computational Biology, Duke-NUS Medical School（杜克-新加坡国立大学医学学校计算生物学中心）；Management and Technology Program, University of Pennsylvania（宾夕法尼亚大学管理与技术项目）；Department of Computer Science, Duke University（杜克大学计算机科学系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）
提出 MOG-DFM 框架，通过混合秩方向分数和自适应超锥滤波引导预训练离散流匹配模型，实现多目标帕累托高效的肽与增强子 DNA 序列设计。
