Retrosynthetic planning is a fundamental problem in chemistry for finding a pathway of reactions to synthesize a target molecule. Recently, search algorithms have shown promising results for solving this problem by using deep neural networks (DNNs) to expand their candidate solutions, i.e., adding new reactions to reaction pathways. However, the existing works on this line are suboptimal; the retrosynthetic planning problem requires the reaction pathways to be (a) represented by real-world reactions and (b) executable using "building block" molecules, yet the DNNs expand reaction pathways without fully incorporating such requirements. Motivated by this, we propose an end-to-end framework for directly training the DNNs towards generating reaction pathways with the desirable properties. Our main idea is based on a self-improving procedure that trains the model to imitate successful trajectories found by itself. We also propose a novel reaction augmentation scheme based on a forward reaction model. Our experiments demonstrate that our scheme significantly improves the success rate of solving the retrosynthetic problem from 86.84% to 96.32% while maintaining the performance of DNN for predicting valid reactions.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Pennsylvania State University（宾夕法尼亚州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Yale University（耶鲁大学）；HEC Montréal（蒙特利尔高等商学院）；Tencent AI Lab（腾讯人工智能实验室）
Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search
SUPSI, IDSIA（SUPSI、IDSIA）；Pfizer Research and Development（辉瑞研发）；Institute for Digital Technologies for Personalized Healthcare（个性化医疗数字技术研究所）；KAUST（王国立科技大学）；Center for Generative AI（生成人工智能中心）
MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning
School of Software, Shandong University（山东大学软件学院）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-新加坡南洋理工大学人工智能联合研究中心（C-FAIR））；Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）
LLM-Augmented Chemical Synthesis and Design Decision Programs
École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院洛桑校区）；National Centre of Competence in Research (NCCR) Catalysis（催化领域竞争力研究国立中心）；Harvard University（哈佛大学）；Cornell University（康奈尔大学）
Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；Manipal Institute of Technology（曼帕尔工程学院）；Rakuten Inc.（乐天株式会社）；Computing and Mathematical Sciences（计算与数学科学学院）
