Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Rensselaer Polytechnic Institute (RPI)（伦斯勒理工学院）；University of Illinois Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Stanford University（斯坦福大学）；Virginia Tech（弗吉尼亚理工大学）
Dannong Wang, Jintai Chen, Zhiding Liang, Tianfan Fu, Xiao-Yang Liu
Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions (i.e., objective) while ensuring synthetic feasibility. However, existing methods are mostly based on random search. To address this issue, in this paper, we introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently. Specifically, we employ a deterministic REINFORCE algorithm using policy neural networks to output transitional probability to guide state transitions and local search using genetic algorithm to refine solutions to a local optimum within each iteration. Our methods are evaluated with the Practical Molecular Optimization (PMO) benchmark framework with a 10K query budget. We further showcase the competitive performance of our method by comparing it against the state-of-the-art genetic algorithms-based method.
Generative Active Learning for the Search of Small-molecule Protein Binders
Mila – Québec AI Institute（米拉-魁北克人工智能研究所）；Dreamfold；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；IRIC, Université de Montréal（蒙特利尔大学IRIC研究所）；Valence Labs（Valence实验室）；X-Chem（X-Chem公司）；MIT（麻省理工学院）；AI Redefined
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
andBeyond（99andBeyond）；Clemson University（克莱姆森大学）；IIIT Hyderabad（海得拉巴国际信息技术学院）；University of Delaware（特拉华大学）；University of Montreal（蒙特利尔大学）；Mila - Quebec AI Institute（Mila - 魁北克人工智能研究所）；HEC Montréal（蒙特利尔高等商学院）
Test-Time Training Scaling Laws for Chemical Exploration in Drug Design
Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain（计算科学实验室，庞培法布拉大学，巴塞罗那生物医学研究公园（PRBB），C Dr. Aiguader 88，08003巴塞罗那，西班牙）
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）
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
National Research Council of Canada（加拿大国家研究委员会）；University of Victoria（维多利亚大学）；University of Waterloo（滑铁卢大学）；Vector Institute for Artificial Intelligence（Vector人工智能研究所）
Assay2Mol: large language model-based drug design using BioAssay context
Department of Computer Sciences, University of Wisconsin-Madison（威斯康星大学麦迪逊分校计算机科学系）；Morgridge Institute for Research（摩尔格德研究所）；Drug Development Core, Small Molecule Screening Facility, University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison（威斯康星大学卡本癌症中心药物开发核心、小分子筛选设施）；Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物统计学与医学信息学系）
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）
SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification
College of Marine Electrical Engineering, Dalian Maritime University（大连海事大学海洋电气工程学院）；AI thrust, Information Hub, Hong Kong University of Science and Technology (GuangZhou)（香港科技大学（广州）人工智能方向、信息中心）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）；Department of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学与技术学院）
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
University of Göttingen（哥廷根大学）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Canadian Institute for Advanced Research（加拿大高等研究院）
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Department of Computer Science, Peking University（北京大学计算机科学系）；Shanghai Jiao Tong University（上海交通大学）；Mila - Québec AI Institute（Mila - 魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Research Chair（CIFAR人工智能研究主席（注：该机构名称为职位头衔，非机构））
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；ByteDance Research（字节跳动研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Research on Intelligent Perception and Computing (CRIPAC)（智能感知与计算研究中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；ByteDance Research（字节跳动研究部）；Halıcıoğlu Data Science Institute, University of California San Diego（加州大学圣迭戈分校哈利西奥格鲁数据科学学院）
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）
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
University of Tokyo（东京大学）；International Research Center for Neurointelligence（国际神经智能研究中心）；RIKEN Center for Biosystems Dynamics Research（理化学研究所生物系统动力学研究中心）
Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学与应用研究所）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州先进研究所）；State Key laboratory of Space Network and Communications, Tsinghua University（清华大学空间网络与通信国家重点实验室）
Quantum-Inspired Machine Learning for Molecular Docking
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；Central Research Institute, 2012 Labs, Huawei Technologies（华为技术有限公司2012实验室中央研究院）；Laboratory of AI for Science, Huawei Cloud Computing Technologies Co., Ltd（华为云计算技术有限公司科学人工智能实验室）；Shenzhen Institute for Quantum Science and Engineering（深圳量子科学与工程研究院）
CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation
Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea（首尔国立大学计算机科学与工程系）；Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea（首尔国立大学生物信息学跨学科项目）；AIGENDRUG Co., Ltd., Seoul, Republic of Korea（AIGENDRUG公司）；Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea（首尔国立大学人工智能跨学科项目）
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Harvard University（哈佛大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））
