Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations. This is especially when relevant when alternate tasks have limited samples of well-defined and labeled data, which is common in the molecule data domain. This makes transfer learning an ideal approach to solve molecular learning tasks. While Adversarial reprogramming has proven to be a successful method to repurpose neural networks for alternate tasks, most works consider source and alternate tasks within the same domain. In this work, we propose a new algorithm, Representation Reprogramming via Dictionary Learning (R2DL), for adversarially reprogramming pretrained language models for molecular learning tasks, motivated by leveraging learned representations in massive state of the art language models. The adversarial program learns a linear transformation between a dense source model input space (language data) and a sparse target model input space (e.g., chemical and biological molecule data) using a k-SVD solver to approximate a sparse representation of the encoded data, via dictionary learning. R2DL achieves the baseline established by state of the art toxicity prediction models trained on domain-specific data and outperforms the baseline in a limited training-data setting, thereby establishing avenues for domain-agnostic transfer learning for tasks with molecule data.
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Shandong University（山东大学）；Oxford University（牛津大学）；Beihang University（北航大学）
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Monash University（莫纳什大学）；Ant Group（蚂蚁集团）；IBM Research（IBM研究院）；Griffith University（格里菲斯大学）；Alibaba Group（阿里巴巴集团）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Renmin University of China（中国人民大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong（香港中文大学）
$\text{M}^{2}$LLM: Multi-view Molecular Representation Learning with Large Language Models
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Department of Data Science and AI, Monash University（莫纳什大学数据科学与人工智能系）；Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University（莫纳什大学药学科学研究所药物发现生物学部）
Explainable Machine Learning Reveals 12-Fold Ucp1 Upregulation and Thermogenic Reprogramming in Female Mouse White Adipose Tissue After 37 Days of Microgravity: First AI/ML Analysis of NASA OSD-970
KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, China（人工智能安全国家重点实验室，计算技术研究所，中国科学院，中国）；University of Chinese Academy of Sciences (CAS), China（中国科学院大学（中国科学院））
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Insilico Medicine Canada Inc.（英矽智能加拿大公司）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Polytechnique Montréal（蒙特利尔工业大学）；Insilico Medicine AI Limited（英矽智能人工智能有限公司）；CIFAR AI Chair（加拿大高级研究院人工智能主席项目）
FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
University of Science and Technology of China（中国科学技术大学）；International Digital Economy Academy（国际数字经济学院）；Peking University（北京大学）；Yale University（耶鲁大学）；National University of Singapore（新加坡国立大学）；Macao Polytechnic University（澳门理工学院）；Zhongguancun Academy（中关村学院）；University of Hong Kong（香港大学）
OLEDLM: A Unified Language Model for OLED Molecular Design
Tsinghua University（清华大学）；Beijing Institute of Mathematical Sciences and Applications（北京应用数学科学研究院）；Wuhan University（武汉大学）；Shenzhen MSU-BIT University（深圳莫斯科国立大学与北京理工大学联合大学）；MathonAI Team（MathonAI团队）
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
Artificial Intelligence Research Laboratory（人工智能研究实验室）；College of Information Sciences and Technology（信息科学与技术学院）；Center for Artificial Intelligence Foundations and Scientific Applications（人工智能基础与科学应用中心）；Institute for Computational and Data Sciences（计算与数据科学研究所）；The Pennsylvania State University（宾夕法尼亚州立大学）
Automated Molecular Concept Generation and Labeling with Large Language Models
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；California Institute of Technology（加州理工学院）；Harvard University（哈佛大学）
Text-guided multi-property molecular optimization with a diffusion language model
School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院）；Engineering Research Center for Big Data Application in Private Health Medicine of Fujian Universities, Putian University, Putian, China（福建省高校大数据在私人医疗应用工程研究中心，莆田大学）
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Department of Computer Science, Tsinghua University（清华大学计算机系）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；PharMolix Inc（PharMolix公司）
