Hi there! I am Mujie Lin(林慕婕), an M.Phil. student in Computer Science at Peking University. Before that, I received my B.S. in Biotechnology from South China University of Technology, with a minor in Computer Science.

My research interests include AI for Science, computational biology, AI-driven drug discovery, and generative modeling for biomolecular dynamics. I am especially interested in building reliable deep learning systems that connect scientific data, foundation models, and deployable research tools.

Research Interests

  • Generative modeling for biomolecular dynamics: spatio-temporal/spatio-spectral, autoregressive, and diffusion-based models for long-horizon protein and molecular dynamics generation, conformational ensemble modeling, and dynamics-aware design.
  • Protein and molecular design: structure-conditioned generation, protein–ligand/co-design, and dynamics-guided biomolecular design.
  • AI-driven drug discovery: molecular representation learning, property and phenotype prediction, virtual screening for medicinal chemistry.
  • Scientific foundation models and evaluation: multimodal and language models for scientific literature understanding, molecular knowledge reasoning, AI4S benchmarks, and agentic evaluation pipelines.

News

  • 2026.06: SyntheticBench (work done during internship at Syneron Bio & KAUST Center of Excellence on Generative AI) accepted to Genomics, Proteomics & Bioinformatics (SCI, JCR Q1 TOP, IF = 13.9).
  • 2026.05: BioDynaSpec (first-author work) accepted to ICML 2026.
  • 2025.11: ProAR accepted to AAAI 2026.
  • 2025.06: Contributed to MiniMax-M1 in the MiniMax Foundation Language Model Team.
  • 2025.05: ADCNet published in Briefings in Bioinformatics (SCI, JCR Q1, IF = 8.7).
  • 2025.01: SciAssess (work done during internship at DP Technology) accepted to NAACL 2025 Findings.
  • 2024.05: MalariaFlow (first-author work) published in European Journal of Medicinal Chemistry (SCI, JCR Q1, IF = 6.4).
  • 2023.11: FG-BERT (second-author work) published in Briefings in Bioinformatics (SCI, JCR Q1, IF = 8.7).

Selected Publications and Preprints

ICML 2026

BioDynaSpec: Harmonic-Guided Spatio-Spectral Autoregressive Diffusion for Protein Dynamics Generation

Mujie Lin, Yutian Liu, Yudi Guo, Yanzhen Hou, Yiheng Tao, Ruochong Zheng, Kaiwen Cheng, Xin Shan, Youdong Mao, Jie Chen

[Paper] [Code]

Spatio-spectral generative modeling for long-horizon protein dynamics, reducing trajectory error by over 60% on ATLAS.

AAAI 2026

ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics

Kaiwen Cheng, Yutian Liu, Zhiwei Nie, Mujie Lin, Yanzhen Hou, Yiheng Tao, Chang Liu, Jie Chen, Youdong Mao, Yonghong Tian

[Paper]

Probabilistic autoregressive generation of molecular dynamics trajectories with anti-drifting sampling for long-horizon stability.

arXiv 2025

MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Aili Chen, Aonian Li, …, Mujie Lin, …, et al.

[Paper] [Code] [Models]

Open-weight hybrid-attention reasoning model. I built automated evaluation infrastructure and reasoning benchmarks for large-scale model iteration.

BIB 2025

ADCNet: a unified framework for predicting the activity of antibody-drug conjugates

Liye Chen#, Biaoshun Li#, Yihao Chen#, Mujie Lin, Shipeng Zhang, Chenxin Li, Yu Pang, Ling Wang

[Paper] [Code] [Webserver]

A unified deep learning framework for antibody-drug conjugate activity prediction, integrating antigen, antibody, linker, payload, and DAR representations.

EJMC 2024

MalariaFlow: A Comprehensive Deep Learning Platform for Multistage Phenotypic Antimalarial Drug Discovery

Mujie Lin, Junxi Cai, Yuancheng Wei, Xinru Peng, Qianhui Luo, Biaoshun Li, Yihao Chen, Ling Wang

[Paper][Webserver]

A curated antimalarial activity prediction platform for multistage phenotypic drug discovery.

arXiv 2024

Uni-SMART: Universal Science Multimodal Analysis and Research Transformer

Hengxing Cai, Xiaochen Cai, Shuwen Yang, Jiankun Wang, Lin Yao, …, Mujie Lin, …, Guolin Ke

[Project] [Paper] [Code]

Multimodal scientific literature understanding for molecules, tables, and charts; featured as Hugging Face Paper of the Day.

BIB 2023

FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction

Biaoshun Li, Mujie Lin, Tiegen Chen, Ling Wang

[Paper] [Code]

Functional-group-based molecular representation learning for transferable molecular property prediction, pertained based on ~1.45 million unlabeled drug-like molecules.

Honors and Awards

  • Chinese National Scholarship, 2022-2023
  • Challenge Cup National Competition, Grand Prize, jointly responsible
  • National College Student Innovation Training Program, Outstanding Completion, project leader

Educations

  • 2025.09 - now, M.Phil. student in Computer Science and Technology, Peking University
  • 2021.09 - 2025.06, B.S. in Biotechnology, South China University of Technology
  • 2022.09 - 2025.06, Minor in Computer Science, South China University of Technology

Internships

  • 2025.02 - 2025.08, LLM Algorithm Intern, Foundation Language Model Team, MiniMax
  • 2024.12 - 2025.03, Machine Learning Research Intern, Syneron Bio & KAUST Center of Excellence on Generative AI
  • 2024.07 - 2024.08, Visiting Student, AI Computational Biology Lab, Westlake University
  • 2023.07 - 2024.03, AI4S Innovative Algorithm Researcher Intern, DP Technology

Academic Service

  • Journal reviewer: Scientific Reports, Bioinformatics, Molecular Diversity
  • Conference reviewer: ACM MM 2026, NeurIPS 2026