The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multi-objective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically-tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.
Generative Molecular Design with Steerable and Granular Synthesizability Control
Laboratory of Artificial Chemical Intelligence (LIAC)（人工化学智能实验室）；NCCR Catalysis（催化联合研究所）；Laboratory of Sustainable and Catalytic Processing (LPDC)（可持续与催化加工实验室）；CHEMSPACE LLC；Enamine Ltd.（Enamine有限公司）；Taras Shevchenko National University of Kyiv（基塔斯·谢甫琴科基辅国立大学）；V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry（V. P. Kukhar生物有机化学与石油化学研究所）；Palladin Institute of Biochemistry（Palladin生物化学研究所）；Laboratory of Catalysis and Organic Synthesis (LCSO)（催化与有机合成实验室）；Laboratory of Protein Design and Immunoengineering (LPDI)（蛋白质设计与免疫工程实验室）
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise
EPFL（洛桑联邦理工学院）；Laboratory of Artificial Chemical Intelligence (LIAC)（人工化学智能实验室（LIAC））；Laboratory of Protein Design and Immunoengineering (LPDI)（蛋白质设计与免疫工程实验室（LPDI））
Molecule AI（分子人工智能公司）；Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Happening Technology Ltd（Happening科技有限公司）；Indian Institute of Technology Delhi（印度理工学院德里分校）
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）
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
George Mason University（乔治梅森大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mila - Université de Montréal（米拉 - 蒙特利尔大学）
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）
Barking up the right tree: an approach to search over molecule synthesis DAGs
University of Cambridge（剑桥大学）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；University College London（伦敦大学学院）；The Alan Turing Institute（艾伦·图灵研究所）；WWU Münster（明斯特大学）；Microsoft Research Cambridge, UK（微软剑桥研究院）
Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
Xinjiang Key Laboratory of Biological Resources and Genetic Engineering（新疆生物资源与基因工程重点实验室）；College of Life Science and Technology, Xinjiang University（新疆大学生命科学与技术学院）；School of Mathematics and System Sciences, Xinjiang University（新疆大学数学与系统科学学院）
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）
Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
PsiDagger；Department of Physics and Astronomy, University of Pittsburgh（物理与天文学系，匹兹堡大学）；Department of Chemistry and The PULSE Institute, Stanford University（化学系和PULSE研究所，斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
Texas A&M University（德克萨斯农工大学）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究所）；Leidos Biomedical Research（莱多斯生物医学研究所）；Brookhaven National Laboratory（布鲁克海文国家实验室）
Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Innovation and Entrepreneurship, Dalian University of Technology（大连理工大学创新创业学院）
Multi-objective generative AI for designing novel brain-targeting small molecules
Harvard University（哈佛大学）；Hugh Kaul Precision Medicine Institute（休·考尔精准医学研究所）；Heersink School of Medicine（赫辛克医学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）
Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks
Wayne State University（韦恩州立大学）；Univ. of Bari Aldo Moro（巴里大学阿尔多·莫罗分校）；National Research Council (CONICET)（国家研究委员会（CONICET））；Daruma Institute for Applied Intelligence（达摩应用智能研究所）
