Deep generative models are attracting great attention for molecular design with desired properties. Most existing models generate molecules by sequentially adding atoms. This often renders generated molecules with less correlation with target properties and low synthetic accessibility. Molecular fragments such as functional groups are more closely related to molecular properties and synthetic accessibility than atoms. Here, we propose a fragment-based molecular generative model which designs new molecules with target properties by sequentially adding molecular fragments to any given starting molecule. A key feature of our model is a high generalization ability in terms of property control and fragment types. The former becomes possible by learning the contribution of individual fragments to the target properties in an auto-regressive manner. For the latter, we used a deep neural network that predicts the bonding probability of two molecules from the embedding vectors of the two molecules as input. The high synthetic accessibility of the generated molecules is implicitly considered while preparing the fragment library with the BRICS decomposition method. We show that the model can generate molecules with the simultaneous control of multiple target properties at a high success rate. It also works equally well with unseen fragments even in the property range where the training data is rare, verifying the high generalization ability. As a practical application, we demonstrated that the model can generate potential inhibitors with high binding affinities against the 3CL protease of SARS-COV-2 in terms of docking score.
A Deep Generative Model for Molecule Optimization via One Fragment Modification
The Ohio State University（俄亥俄州立大学）；NEC Labs America（NEC美国实验室）；Translational Data Analytics Institute, The Ohio State University（俄亥俄州立大学转化数据分析研究所）；Biomedical Informatics, The Ohio State University（俄亥俄州立大学生物医学信息学）
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
Department of Chemistry, KAIST（韩国科学技术院化学系）；InnoCORE AI-CRED Institute, KAIST（韩国科学技术院InnoCORE AI-CRED研究所）；College of Pharmacy, Dongguk University（东国大学药学院）；Department of Data Science, KAIST（韩国科学技术院数据科学系）
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Vancouver Prostate Centre, The University of British Columbia（温哥华前列腺中心、不列颠哥伦比亚大学）；Mila - Quebec AI Institute, Université de Montréal（魁北克人工智能研究所、蒙特利尔大学）；Simon Fraser University（西蒙弗雷泽大学）
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Tsinghua University（清华大学）；University of Washington（华盛顿大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；ByteDance Inc.（字节跳动公司）；Helixon Inc.（Helixon公司）
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；College of Foreign Languages and Literatures, Fudan University（复旦大学外国语言文学学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究所）
