Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery lack the ability to comprehend three-dimensional (3D) structures, thereby limiting their effectiveness in tasks that explicitly involve molecular conformations. In this study, we introduced Token-Mol, a token-only 3D drug design model. This model encodes all molecular information, including 2D and 3D structures, as well as molecular property data, into tokens, which transforms classification and regression tasks in drug discovery into probabilistic prediction problems, thereby enabling learning through a unified paradigm. Token-Mol is built on the transformer decoder architecture and trained using random causal masking techniques. Additionally, we proposed the Gaussian cross-entropy (GCE) loss function to overcome the challenges in regression tasks, significantly enhancing the capacity of LLMs to learn continuous numerical values. Through a combination of fine-tuning and reinforcement learning (RL), Token-Mol achieves performance comparable to or surpassing existing task-specific methods across various downstream tasks, including pocket-based molecular generation, conformation generation, and molecular property prediction. Compared to existing molecular pre-trained models, Token-Mol exhibits superior proficiency in handling a wider range of downstream tasks essential for drug design. Notably, our approach improves regression task accuracy by approximately 30% compared to similar token-only methods. Token-Mol overcomes the precision limitations of token-only models and has the potential to integrate seamlessly with general models such as ChatGPT, paving the way for the development of a universal artificial intelligence drug design model that facilitates rapid and high-quality drug design by experts.
Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model
West China Hospital, Sichuan University（四川大学华西医院）；University of Gothenburg（哥德堡大学）；The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)（中国科学院大学附属肿瘤医院（浙江省肿瘤医院））；Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences（中国科学院基础医学与肿瘤研究所（IBMC））
Diversity-Aware Reinforcement Learning for de novo Drug Design
Chalmers University of Technology and University of Gothenburg（查尔姆斯理工大学和哥德堡大学）；Molecular AI, Discovery Sciences, R&D, AstraZeneca（分子人工智能、发现科学、研发、阿斯利康）；Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca（药物化学、研究与早期开发、呼吸系统与免疫学（R&I）、生物制药研发、阿斯利康）
Test-Time Training Scaling Laws for Chemical Exploration in Drug Design
Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain（计算科学实验室，庞培法布拉大学，巴塞罗那生物医学研究公园（PRBB），C Dr. Aiguader 88，08003巴塞罗那，西班牙）
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer
Ping An Healthcare Technology（平安医疗科技）；Chinese Academy of Medical Sciences（中国医学科学院）；National Cancer Center（国家癌症中心）；Xidian University School of Computer Science and Technology（西安电子科技大学计算机科学与技术学院）
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）
Research and development of MolAICal for drug design via deep learning and classical programming
深度学习方法已经渗透到计算机辅助药物设计的研究领域。深度学习生成模型和经典算法可以同时用于受体3D口袋中的3D药物设计。在这里，阐述了MolAICal用于药物设计的三个方面：在第一部分中，MolAICal使用遗传算法，Vinardo评分和由生成对抗网（GAN）训练的深度学习生成模型进行药物设计。在第二部分中，深度学习生成模型由来自药物数据库（如ZINC数据库）的药物样分子训练。MolAICal自动调用深度学习生成模型和分子对接进行药物虚拟筛选。在第三部分中，我们增加了药物相关性质的计算工具，如PAINS、Lipinski's rule of five、合成可及性（SA）等，并将结构相似性搜索和定量构效关系（QSAR）等嵌入到MolAICal中。MolAICal将不断优化和开发用于药物设计的现有和新模块。MolAICal可以帮助科学家，药剂师和生物学家通过深度学习模型和经典编程设计受体口袋中的合理3D药物。MolAICal对于任何学术和教育目的都是免费的，可以从网站https://molaical.github.io下载。
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系生物数据科学实验室）；Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系）；Dept. of Chemistry, Middle East Technical University（中东技术大学化学系）；Dept. of Physics, Middle East Technical University（中东技术大学物理系）；Dept. Of Artificial Intelligence Engineering, Bahcesehir University（巴切塞希尔大学人工智能工程系）；Cancer Systems Biology Lab, Graduate School of Informatics, Middle East Technical University（中东技术大学信息研究生院癌症系统生物学实验室）；Dept. of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University（加齐大学药学系药剂化学系）；Laboratory of Molecular Modeling, Evias Pharmaceutical R&D Ltd.（Evias制药研发有限公司分子建模实验室）；Dept. of Electrical and Electronics Engineering, İskenderun Technical University（伊斯肯德伦技术大学电子与电气工程系）；Institute for Computational Biomedicine, Heidelberg University（海德堡大学计算生物医学研究所）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（哈切塔佩大学健康科学研究生院生物信息学系）
Associative Learning Mechanism for Drug-Target Interaction Prediction
Chongqing University of Posts and Telecommunications（重庆邮电大学）；State University of New York at Buffalo State（纽约州立大学布法罗州立学院）；DH (Shanghai) Diagnostics Co.,Ltd.（DH（上海）诊断有限公司）
Vertex-Edge Weighted Molecular Graphs: A study on topological indices and their relevance to physicochemical properties of drugs in use cancer treatment
Towards a Better Model with Dual Transformer for Drug Response Prediction
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）；Mechanical Engineering Research Institute of the Russian Academy of Sciences（俄罗斯科学院机械工程研究所）；Hubei Cancer Hospital（湖北省肿瘤医院）；Renmin Hospital of Wuhan University（武汉大学人民医院）；Wuhan University Shenzhen Research Institute（武汉大学深圳研究院）
Molecular docking and binding mode analysis of selected FDA approved drugs against COVID-19 selected key protein targets: An effort towards drug repurposing to identify the combination therapy to combat COVID-19
Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University（北京大学药学院天然药物及仿生药物国家重点实验室）；State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University（南京大学医药生物技术国家重点实验室）
Molexar: A Unified Multimodal Molecular Foundation Model for Drug Design
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院定量生物学中心）；School of Life Sciences, Peking University（北京大学生命科学学院）；Department of Pathology, The First Affiliated Hospital of Shantou University Medical College（汕头大学医学院第一附属医院病理科）；BNLMS, College of Chemistry and Molecular Engineering, Peking University（北京大学化学与分子工程学院北京分子科学国家研究中心）；Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院清华-北大生命科学中心）；Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies（北京大学成都前沿交叉生物技术研究院）
Fragment-Masked Diffusion for Molecular Optimization
School of Computer Science, Wuhan University（武汉大学计算机学院）；Department of Computing, Macquarie University（麦考瑞大学计算机系）；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 Abdominal Oncology, Hubei Cancer Hospital（湖北癌症医院腹部肿瘤科）
