Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
Centre of New Technologies, University of Warsaw（华沙大学新技术中心）；Institute of Organic Chemistry, Polish Academy of Sciences（波兰科学院有机化学研究所）
Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Dąbrowski-Tumański, Mikołaj Chromiński, Rafał Loska, Paweł Włodarczyk-Pruszyński, Stanisław Jastrzębski
The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large datasets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion, and achieving state-of-the-art accuracy in standard benchmarks. Code and trained models are made available online at https://github.com/molecule-one/megan.
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；NVIDIA Research（英伟达研究院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；California Institute of Technology（加州理工学院）；Princeton University（普林斯顿大学）；HEC Montréal（蒙特利尔高等商学院）；Arizona State University（亚利桑那州立大学）
MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning
Artificial Intelligence Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州）人工智能研究方向）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
Research Organization of Information and Systems（信息与系统研究机构）；The Institute of Statistical Mathematics（统计数学研究所）；The Graduate University for Advanced Studies, SOKENDAI（综合研究大学院大学）；National Institute for Materials Science（国立材料科学研究所）
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University（清华大学交叉信息研究院）；Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）
Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data
Zhejiang University of Technology（浙江工业大学）；Macao Polytechnic University（澳门理工大学）
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；University of Copenhagen（哥本哈根大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
University of Toronto（多伦多大学）；The Hospital for Sick Children（多伦多儿童医院）；University of Cambridge（剑桥大学）；ETH Zürich（苏黎世联邦理工学院）；Vector Institute（向量研究所）；Mila – Quebec AI Institute（魁北克AI研究所）；McGill University（麦吉尔大学）；Caltech（加州理工学院）
