AI for drug discovery has been a research hotspot in recent years, and SMILES-based language models has been increasingly applied in drug molecular design. However, no work has explored whether and how language models understand the chemical spatial structure from 1D sequences. In this work, we pre-train a transformer model on chemical language and fine-tune it toward drug design objectives, and investigate the correspondence between high-frequency SMILES substrings and molecular fragments. The results indicate that language models can understand chemical structures from the perspective of molecular fragments, and the structural knowledge learned through fine-tuning is reflected in the high-frequency SMILES substrings generated by the model.
A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery
Advanced Technology Core for Macromolecular X-Ray Crystallography（宏分子X射线衍射先进技术核心）；Verna and Marrs McLean Dept. of Biochemistry and Molecular Pharmacology（韦纳和玛里斯麦克莱恩生物化学与分子药理学系）；Dept. of Molecular & Cellular Biology（分子与细胞生物学系）；Dept. of Molecular Virology & Microbiology（分子病毒学与微生物学系）；Baylor College of Medicine（贝勒医学院）
FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）；Pritzker School of Molecular Engineering, University of Chicago（芝加哥大学分子工程学院）；Department of Chemistry, University of Chicago（芝加哥大学化学系）；Argonne National Laboratory（阿贡国家实验室）；AI Lab, Princeton University（普林斯顿大学人工智能实验室）
DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
Yale University（耶鲁大学）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）；The Chinese University of Hong Kong（香港中文大学）；University of California, Berkeley（加州大学伯克利分校）；UC Berkeley（加州大学伯克利分校）
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
School of Computing Science（计算科学学院）；School of Cancer Sciences（癌症科学学院）；Cancer Research UK Scotland Institute（英国癌症研究苏格兰研究所）；University of Glasgow（格拉斯哥大学）
Large Language Model Agent for Modular Task Execution in Drug Discovery
Department of Chemical Engineering, Carnegie Mellon University（化学工程系，卡内基梅隆大学）；Department of Material Science and Engineering, Carnegie Mellon University（材料科学与工程系，卡内基梅隆大学）；Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
MOE Key Laboratory of Bioinformatics（教育部生物信息学重点实验室）；State Key Laboratory of Molecular Oncology（分子肿瘤学国家重点实验室）；Beijing Frontier Research Center for Biological Structure（北京前沿生物结构研究中心）；School of Pharmaceutical Sciences, Tsinghua University（清华大学药学院）
DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Stevens Institute of Technology（史蒂文斯理工学院）；Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）
DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model
Regenerative Medicine Research Center, Isfahan University of Medical Sciences（再生医学研究中心，伊斯法罕医科大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences（伊斯法罕神经科学研究中心，伊斯法罕医科大学）；Department of Electrical and Computer Engineering, Isfahan University of Technology（电气与计算机工程系，伊斯法罕技术大学）
Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Ecole Polytechnique Fédérale de Lausanne（瑞士联邦理工学院）；Oxford University（牛津大学）；VantAI；Aithyra Research Institute for Biomedical Artificial Intelligence（Aithyra生物医学人工智能研究所）；National Centre of Competence in Research (NCCR) Catalysis EPFL, Switzerland（瑞士国家研究能力中心（NCCR）催化EPFL，瑞士）
LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison
Software Engineering (SeSa) Lab（软件工程（SeSa）实验室）；University of Salerno（萨勒诺大学）；Department of Epidemiology and Data Science（流行病学与数据科学系）；Amsterdam UMC Locatie AMC（阿姆斯特丹大学医学中心（AMC））；Amsterdam Public Health Research Institute（阿姆斯特丹公共卫生研究院）；University of Amsterdam（阿姆斯特丹大学）；University of Amsterdam Data Science Center（阿姆斯特丹大学数据科学中心）
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；School of Information and Software Engineering, UESTC（电子科技大学信息与软件工程学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
College of Computer Science, Sichuan University（四川大学计算机学院）；West China Hospital, Sichuan University（四川大学华西医院）；Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences（北京农林科学院信息技术研究中心）
Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室）；Department of Computer Science, Aalto University（艾尔沃斯大学计算机科学系）；College of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
