AI × Molecular Science

Jiatong Li

Building AI co-scientists for molecular discovery.

I work on molecular representations, chemical reasoning, and tool-using agents. My goal is to build AI co-scientists that help researchers explore and test molecular ideas.

Latest updates

News

Follow the work ↗

MolLean is now online, introducing a process-verifiable language for chemical reasoning with a Lean-based kernel and interactive verification. Read the paper ↗

STAMP is now online, presenting a topology-aware molecular language for fragment-level learning and precise structural editing. Read the paper ↗

I am serving as a Teaching Assistant for COMP5584, Large Language Models for Agents, at The Hong Kong Polytechnic University this semester.

I will return to NUS as an incoming Research Fellow with the BlueWhale Lab ↗, following my Visiting Scholar appointment from January to July 2026.

Earlier notes (8)Close earlier notes

MolViBench is released, introducing a benchmark for molecular vibe coding across real-world drug-discovery workflows. Code ↗

MolReFlect is accepted by IEEE TKDE. Code ↗

CMPhysBench is accepted by ICLR 2026. Paper ↗

AI co-scientist research agenda

Research directions

My research connects molecular representations, chemical reasoning, and scientific tool use. These four questions guide the work.

01 / The research question

How can an AI maintain an explicit molecular state?

I study representations that connect molecular structures, fragments, and language. These representations let a model describe a molecule and specify structural edits.

Start with an explicit molecular state.

02 / The research question

How can we verify each step of chemical reasoning?

I develop methods for checking intermediate molecular states and chemical operations. I also study how chain-of-thought explanations relate to a model’s predictions.

Verify the intermediate steps.

03 / The research question

How can agents carry out molecular research tasks?

I study agents that translate molecular design goals into tool calls and code. My evaluations test the resulting programs on drug-discovery workflows.

Connect research questions to tools and code.

04 / The research question

How do models behave on unfamiliar scientific problems?

I test models with molecular graph edits and problems from physics and medicine to understand where their predictions change or fail.

Test beyond familiar examples.

Each direction links to the papers behind it.

Selected

Recent publications

Recent papers on molecular representations, chemical reasoning, and scientific agents.

  1. 2026arXiv
  2. 2026arXiv
  3. 2026

    Molecular LLM Agents: From Architectural Design to Scientific Autonomy

    Jiatong Li, Wengyu Zhang, Weida Wang, Yuxuan Ren, Wei Liu, Chenyang Mao, Yuqiang Li, Yatao Bian, Changmeng Zheng, Xiaoyong Wei, Qing Li

    arXiv
  4. 2026

    Chemical Chain-of-Thought Functions as a Hallucination-Prone Molecular Scratchpad

    Jiatong Li, Yuxuan Ren, Weida Wang, Xiaoyong Wei, Yatao Bian

    arXiv
  5. 2026

    Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis

    Jiatong Li, Weida Wang, Changmeng Zheng, Shufei Zhang, Yatao Bian, Xiao-yong Wei, Qing Li

    arXiv
  6. 2026

    Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

    Qi Peng*, Jiatong Li*, Sirui Huang, Yiyang Jiang, Kaisong Gong, et al.

    MIR
  7. 2026

    MolViBench: Evaluating LLMs on Molecular Vibe Coding

    Jiatong Li, Yuxuan Ren, Weida Wang, Changmeng Zheng, Xiao-yong Wei, Qing Li, Yatao Bian

    arXiv
  8. 2026

    MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts

    Jiatong Li, Yunqing Liu, Wei Liu, Jingdi Lei, Di Zhang, Wenqi Fan, Dongzhan Zhou, Yuqiang Li, Qing Li

    IEEE TKDE
  9. 2026

    Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation

    Jiatong Li, Junxian Li, Weida Wang, Yunqing Liu, Changmeng Zheng, Yatao Bian, et al.

    KDD
  10. 2026

    Chem-R: Learning to Reason as a Chemist

    Weida Wang, Benteng Chen, Di Zhang, et al., Jiatong Li, et al. · Co-corresponding author

    KDD
  11. 2026

    CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics

    Weida Wang*, Dongchen Huang*, Jiatong Li*, Tengchao Yang, et al. · Co-first author

    ICLR
  12. 2026

    IAG: Input-Aware Backdoor Attack on VLM-Based Visual Grounding

    Junxian Li, Beining Xu, Simin Chen, Jiatong Li, Jingdi Lei, Haodong Zhao, Di Zhang

    CVPR
  13. 2025

    Large Language Models are In-Context Molecule Learners

    Jiatong Li, Wei Liu, Zhihao Ding, Wenqi Fan, Yuqiang Li, Qing Li

    IEEE TKDE
  14. 2025

    Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery

    Jiatong Li, Weida Wang, Qinggang Zhang, Junxian Li, Di Zhang, Changmeng Zheng, et al.

    arXiv
Explore the full publication archive

Background

Research journey

Training across information security, natural language processing, and AI-driven molecular discovery.

Education

  1. 2023–Present Ph.D. Candidate, Computing

    The Hong Kong Polytechnic University · HKPFS

  2. 2021–2022 Master of Information Technology

    The University of Melbourne · Dean’s Honor List

  3. 2017–2021 B.Eng. in Information Security

    Shanghai Jiao Tong University

Research experience

  1. Incoming Research Fellow

    BlueWhale Lab ↗ · National University of Singapore

  2. 2026.01–07 Visiting Scholar

    BlueWhale Lab ↗ · National University of Singapore

  3. 2023–2024 Algorithm Researcher

    Shanghai AI Laboratory

  4. 2021–2022 Algorithm Researcher

    Noah’s Ark Lab

Beyond papers

Teaching & service

Teaching

Teaching Assistant · PolyU

  • COMP5584 Large Language Models for Agents · 2026/27 Semester 1
  • COMP2411 Database Systems
  • COMP4434 Big Data Analytics
  • COMP6703 Advanced Topics in Data Analytics
  • COMP6713 Advanced Large Language Models and Beyond
Academic service

Program committees & reviewing

PC: AAAI 2024–2026, IIAI AAI 2026, PRICAI 2024–2025, WISE 2024.

Reviewer: ICLR, NeurIPS, ICML, ACL, KDD, AISTATS, IEEE TKDE, Journal of Cheminformatics, npj Digital Medicine, and more.

Recognition

Honors

2022 Hong Kong PhD Fellowship Scheme

HKSAR Government

2021 Melbourne Graduate Grant

The University of Melbourne

2020 Finalist Award · MCM

Mathematical Contest in Modeling

On repeat

A little life beyond the lab.

A song for the hours between molecules and models.

Collaborate

Interested in AI for molecular science?

I welcome conversations about chemical reasoning, scientific agents, and research collaborations.

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