Retrosynthesis

Latest papers

23 papers6 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction
Oct 14, 2026Yanyi Su, Yu Zeng, Hongshuai Wang +3Chemical Reasoning in LLMs Retrosynthesis
State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China · DP Technology, Beijing, China
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning
Oct 12, 2026Ye Rin Kim, Yongjun Jeong, Seongjun Yun +4Retrosynthesis LLM Planning
Korea University, Department of Artificial Intelligence · Korea University, Department of Statistics
Single-step Retrosynthesis via Reinforcement Learning from Forward Feedback
Oct 10, 2026Zixun Lan, Zhenfu Liu, Yi Li +6Retrosynthesis Reinforcement Learning
Xi’an Jiaotong-Liverpool University, Suzhou 215000, China · Sichuan University of Science & Engineering, Zigong 643000, China · Fudan University, Shanghai 200433, China +1
Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis
Sep 30, 2026Xuemin Chen, Xiaozhuang Song, Xinjian Zhao +2Instruction Following Instruction Tuning
The Chinese University of Hong Kong, Shenzhen
RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis
Sep 29, 2026Xiaozhuang Song, Xuemin Chen, Xinjian Zhao +2One-Step Generative Modeling Molecular Generation
The Chinese University of Hong Kong, Shenzhen · Shanghai AI Laboratory
Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning
Sep 28, 2026Hyunwoo Yoo, Cassie Huang, Haebin Shin +2LLM Planning Symbolic Planning
Drexel University · University of Michigan
Rachel: A general-purpose language model directs and revises retrosynthetic routes
Sep 20, 2026Qisheng Li, Shunchao Jiang, Chen Qi +3Retrosynthesis LLM Planning
Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China · Beijing Advanced Innovation Center for Soft Matter Science and Engineering and State Key Laboratory of Organic–Inorganic Composites, Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China · Zhenzhida Biotechnology (Shanghai) Co., Ltd. +3
An Agentic Retrobiosynthesis Framework with Learned Frontier Selection
Aug 31, 2026Philippe Meyer, Guillaume Gricourt, Thomas Duigou +2Frontier-Based Exploration Large Language Model-Guided Scientific Discovery
1Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis
Aug 19, 2026Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev +5Chemical Reaction Prediction Retrosynthesis
Insilico Medicine AI Limited, Abu Dhabi, UAE · Insilico Medicine Canada Inc., Montreal, Quebec, Canada · Insilico Medicine Hong Kong Ltd., Hong Kong SAR, China
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
Aug 17, 2026Mianzhi Liu, Fan Xiao, Zhiliang Yu +5Retrosynthesis AI-Assisted Scientific Research
School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China · School of Computer Science, Wuhan University, Wuhan 430072, China · College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China +1
RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning
Jul 16, 2026Yanqiao Zhu, Jingru Gan, Xiaoqi Sun +8LLM Agent Harnesses LLM Agent Planning
UCLA · MIT · Amazon +1
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
Jul 8, 2026Chen Tang, Yizhou Wang, Jianyu Wu +26Scientific Reasoning Materials Property Prediction
Shanghai Artificial Intelligence Laboratory, China. · The Chinese University of Hong Kong, Hong Kong. · Shanghai Jiao Tong University, China. +8
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Jul 6, 2026Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev +7LLM Evaluation LLM Planning
Insilico Medicine AI Limited, Masdar City, Abu Dhabi, UAE · Independent researcher · Insilico Medicine Canada Inc., Montreal, Quebec, Canada +1
Project Ariadne: Prompt-Conditioned Route Generation for Synthesis Planning
Jun 23, 2026Anton Morgunov, Victor S. BatistaAutoregressive Generation Retrosynthesis
Yale University
RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking
Jun 5, 2026Raja Sekhar Pappala, Shreyas Vinaya Sathyanarayana, Ronit Kumar Choudhary +2Transformer Learning to Rank
Mstack AI
Representation-Guided Discrete Molecular Graph Retrosynthesis
May 23, 2026Jiahai Huang, Anjie Qiao, Zhen Wang +2Diffusion Model Guidance Molecular Graph Generation
Sun Yat-sen University Guangzhou, China · University of Science and Technology of China Hefei, China
Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning
May 13, 2026Najwa Laabid, Vikas GargMargin-Based Learning Retrosynthesis
Department of Computer Science Aalto University · YaiYai Ltd
ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis
May 12, 2026Mohammad Jahid Ibna Basher, Ali Khodabandeh Yalabadi, Ivan Garibay +1Dense Retrieval Retrosynthesis
Department of Industrial Engineering University of Central Florida Orlando, FL 32816
From Feasible to Practical: Pareto-Optimal Synthesis Planning
May 8, 2026Friedrich Hastedt, Dongda Zhang, Antonio del Rio ChanonaMulti-Objective Optimization Pareto Optimization
Department of Chemical Engineering, Imperial College London, UK · Department of Chemical Engineering, University of Manchester, UK
Computational framework for multistep metabolic pathway design
Apr 15, 2026Peter Zhiping Zhang, Jeffrey D. VarnerRetrosynthesis
Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca, NY 14850
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
Mar 13, 2026Hanbum Ko, Chanhui Lee, Ye Rin Kim +4AI-Assisted Scientific Research Language Modeling
Department of Artificial Intelligence, Korea University · Materials Intelligence Lab, LG AI Research · Department of Statistics, Korea University
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
Mar 3, 2026Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17Molecular Optimization Molecular Property Prediction
Insilico Medicine · Liquid AI
Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation
Aug 7, 2025Kartar Kumar, Rajesh Kumar, Nikesh LagunAI-Assisted Scientific Research Laboratory Automation
Department of Computer Science, Institute of Business Administration (IBA), Karachi, Pakistan · International Research Center for Complexity Sciences, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China · School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Oct 14, 2026J/K move · Enter open · S save

Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction

Yanyi Su, Yu Zeng, Hongshuai Wang, Chenyan Wang+2

State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China · DP Technology, Beijing, China

Chemical Reasoning in LLMs Retrosynthesis LLM Evaluation Molecular Property Prediction

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LLMs have achieved strong performance on chemical prediction benchmarks. However, existing evaluation methods typically provide target entity identity information directly, making it difficult to determine whether model predictions arise from transferable structure-property reasoning capabilities or rely on target-related contextual information or memory retrieval. We propose the Identity-Blinded Reasoning Diagnostic framework to evaluate chemical prediction models when target entity identity information is hidden. In this framework, the target is no longer directly defined by its entity identity, but instead by a known source entity and an explicit structural edit operation. This setting preserves the information required to determine the target while reducing the opportunity for models to access target identity cues. We evaluate this framework across six tasks spanning odor prediction, molecular property prediction, retrosynthesis, and reaction yield prediction. The results show that models generally achieve better prediction performance when source and target entity properties are consistent. Providing explicit source entity labels further strengthens this dependence. Moreover, while keeping the target entity, structural change, and ground-truth label unchanged, replacing only the source entity label causes some model predictions to systematically shift toward the substituted source labels. These results indicate that high benchmark accuracy in chemical prediction tasks does not necessarily imply that LLMs possess reliable structure-property reasoning capabilities. Our diagnostic framework provides a controlled evaluation approach for testing whether models can genuinely update predictions according to entity changes. The framework can be extended to other AI for Science tasks, particularly those where targets can be defined through transformations of known entities.