Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
University of Pittsburgh（匹兹堡大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences（波兰科学院耶日·哈伯催化与表面化学研究所）
David Oniani, Jordan Hilsman, Chengxi Zang, Junmei Wang, Lianjin Cai, Jan Zawala, Yanshan Wang
A drug molecule is a substance that changes the organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications, or vice versa, which could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, which is the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.
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）
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆）；National Cancer Institute (NCI), National Institutes of Health (NIH)（美国国立卫生研究院国家癌症研究所）；Dartmouth Health（达特茅斯医疗系统）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT
MGEN -- College of Engineering, Northeastern University Miami（东北大学迈阿密分校MGEN工程学院）；University of Vermont（佛蒙特大学）；AGH, University of Krakow（克拉科夫AGH大学）；Howard University（霍华德大学）
RxSafeBench: Identifying Medication Safety Issues of Large Language Models in Simulated Consultation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Electronic Science and Technology of China（电子科技大学）；Shenzhen University of Advanced Technology（深圳大学）；School of Biomedical Engineering, UNSW Sydney（生物医学工程学院，UNSW悉尼）
Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners
Charmo University（查尔莫大学）；Komar University of Science and Technology（科马尔科技大学）；University of Sulaimani（苏莱曼尼亚大学）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；Eskisehir Osmangazi University（埃斯基谢希尔奥斯曼加齐大学）
Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Institute of Open Source Software and Platform, Peng Cheng Laboratory（鹏城实验室开源软件与平台研究所）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University（湖南大学化学生物传感与计量学国家重点实验室）
Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery
Ph.D. Program in Computer Science The Graduate Center The City University of New York（计算机科学博士项目 美国城市大学研究生中心 新 York 美国）；School of Data Science & Department of Biomedical Engineering University of Virginia（数据科学学院与生物医学工程系 美国弗吉尼亚大学）；School of Pharmacy and Pharmaceutical Sciences & Center for Drug Discovery Northeastern University（药学与药学科学学院及药物发现中心 美国东北大学）；Department of Computer Science Hunter College The City University of New York（计算机科学系 美国城市大学霍顿学院）；Helen & Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University（海伦与罗伯特·阿普尔阿尔茨海默病研究所 费尔家族脑与心灵研究研究所 威尔·康奈尔医学院 康奈尔大学）
