The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.
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）
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）
Utilising Graph Machine Learning within Drug Discovery and Development
Relation Therapeutics（Relay Therapeutics（注：原文为Relation Therapeutics，直译：关系治疗公司））；University of Cambridge（剑桥大学）；Juvenescence（Juvenescence公司）；Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；HEC Montreal（蒙特利尔高等商学院）；The Francis Crick Institute（弗朗西斯·克里克研究所）；Imperial College London（帝国理工学院）；Twitter（推特）
Rensselaer Polytechnic Institute（拉特格斯理工大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Pittsburgh（匹兹堡大学）；University of Chicago（芝加哥大学）；Novo Nordisk A/S（诺和制药）；Harvard University（哈佛大学）
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）
Department of Computer and Data Sciences, Case Western Reserve University（计算机与数据科学系，凯斯西储大学）；Case Western Reserve University（凯斯西储大学）；Department of Biochemistry, Case Western Reserve University（生物化学系，凯斯西储大学）；Center for RNA Science and Therapeutics, Case Western Reserve University（RNA科学与治疗中心，凯斯西储大学）；Department of Biomedical Engineering, Case Western Reserve University（生物医学工程系，凯斯西储大学）
BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery
Department of Statistics University of South Carolina（统计学系南卡罗来纳大学）；Department of Biostatistics University of South Carolina（生物统计学系南卡罗来纳大学）；Department of Biostatistics University of Florida（生物统计学系佛罗里达大学）；Department of Surgery University of Florida（外科医学系佛罗里达大学）；Department of Statistics George Mason University（统计学系乔治·梅森大学）
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）；Peking University（北京大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
DrugGen 2: A disease-aware language model for enhancing drug discovery
Regenerative Medicine Research Center（再生医学研究中心）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center（伊斯法罕神经科学研究中心）；School of Pharmacy（药学院）；Department of Medicinal Chemistry（药理化学系）；Cardiovascular Research Institute（心血管研究学院）；Department of Genetics and Molecular Biology（基因与分子生物学系）；Bioinformatics Research Center（生物信息学研究中心）
REINFORCE-ING Chemical Language Models for Drug Discovery
Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain；In Silico Discovery, Janssen Research \& Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
University College London（伦敦大学学院）；Università di Napoli Federico II（那不勒斯费德里科二世大学）；University of Chicago（芝加哥大学）；RWTH Aachen University（亚琛工业大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Rutgers, the State University of New Jersey（罗格斯大学，新泽西州立大学）；Science Museum（科学博物馆）；Italian Institute of Technology（意大利理工学院）；Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities（巴伐利亚科学与人文学院莱布尼茨超级计算中心）；CERN（欧洲核子研究中心）；Argonne National Laboratory（阿贡国家实验室）；University of Amsterdam（阿姆斯特丹大学）
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Edwardson School of Industrial Engineering and the Institute of Cancer Research, Purdue University（埃德沃兹工业工程学院和癌症研究所，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家庭电气与计算机工程学院，普渡大学）
How Evaluation Choices Distort the Outcome of Generative Drug Discovery
Institute for Complex Molecular Systems（复杂分子系统研究所）；Dept. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands（生物医学工程系，埃因霍温理工大学，埃因霍温，荷兰）；Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, The Netherlands（生命技术中心，联盟TU/e，WUR，UU，乌得勒支大学医疗中心，荷兰）
