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）
Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning
Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size; and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modifying one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement as at least 17.8% better than Modof-pipe.
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）
t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University（湖南大学化学生物传感与计量学国家重点实验室，化学化工学院）
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究院）；Harvard University（哈佛大学）；Canadian Institute for Advanced Research（加拿大高级研究院）
De Novo Molecular Generation via Connection-aware Motif Mining
University of Science and Technology of China（中国科学技术大学）；Renmin University of China（中国人民大学）；Microsoft Research AI4Science（微软研究院AI4Science）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada（电气与计算机工程系，多伦多大学，多伦多，加拿大）；Regenerative Biomaterials Laboratory, Stanford Cardiovascular Institute, Palo Alto, USA（再生生物材料实验室，斯坦福心血管研究所，帕洛阿尔托，美国）
Fast Leave-One-Out Approximation from Fragment-Target Prevalence Vectors (molFTP) : From Dummy Masking to Key-LOO for Leakage-Free Feature Construction
An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
MOE Key Laboratory of Bioinformatics（教育部生物信息学重点实验室）；State Key Laboratory of Molecular Oncology（分子肿瘤学国家重点实验室）；Beijing Frontier Research Center for Biological Structure（北京前沿生物结构研究中心）；School of Pharmaceutical Sciences, Tsinghua University（清华大学药学院）
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）
3D Molecule Generation from Rigid Motifs via SE(3) Flows
School of Computation, Information and Technology, Technical University of Munich, Germany（计算、信息与技术学院，慕尼黑技术大学，德国）；Munich Data Science Institute (MDSI), Technical University of Munich, Germany（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学，德国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）
FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）；Pritzker School of Molecular Engineering, University of Chicago（芝加哥大学分子工程学院）；Department of Chemistry, University of Chicago（芝加哥大学化学系）；Argonne National Laboratory（阿贡国家实验室）；AI Lab, Princeton University（普林斯顿大学人工智能实验室）
Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation
SCNU Environmental Research Institute（华南师范大学环境研究院）；Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety（广东省化学污染与环境安全重点实验室）；School of Environment, South China Normal University（华南师范大学环境学院）；MOE Key Laboratory of Environmental Theoretical Chemistry（教育部环境理论化学重点实验室）
