Laboratory Introduction
Dr. Fan is an Investigator at Chinese Institute for Molecular and Cellular Therapeutics, CIMR. He earned his B.S. from the University of Science and Technology of China (USTC) and his Ph.D. from the University of Groningen. He conducted his postdoctoral and research scientist work at the University of California, San Francisco (UCSF). Prior to joining CIMR, he served as a Senior Principal Investigator at the Bioinformatics Institute, A*STAR, Singapore.
The Fan Lab drives innovation in Molecular Therapeutics through a "Dual-Engine" approach:
· AI-Driven Discovery: Leveraging Predictive and Generative AI to accelerate the development of small-molecule drugs and functional proteins.
· Physics-Based Refinement: Utilizing high-precision computational chemistry and drug design (e.g., Free Energy Perturbation/FEP, Molecular Dynamics) to provide physical grounding and accurate validation.
These two pillars complement each other to bridge the gap between AI-generated designs and experimental reality.
Recent Research Highlights
1. AI-Driven Enzyme Design: Developed machine learning workflows to explore the substrate scope of galactose oxidase (ACS Catalysis 2024) and engineered novel fluorinases (Chemical Science 2025).
2. Generative AI for Medicine: Created "TCM-Navigator," the first deep-learning-based end-to-end workflow for optimizing Traditional Chinese Medicine chemical spaces (Briefings in Bioinformatics 2025).
3. GPCR & Kinase Mechanisms: Elucidated the molecular basis of GPR84 selectivity (Nat Comm 2023) and the resistance mechanisms of BRAF mutations (Science Advances 2021).
4. Precision Ligand Discovery: Developed a contrastive neural network-based AI method for ligand prediction against general protein targets (https://www.biorxiv.org/content/10.1101/2025.03.16.643501v2).
Laboratory website: https://www.cimrbj.ac.cn/en/channel/2013512987888979968.html
Open Positions: AI / Algorithm Engineer (1–2 positions)
Main Responsibilities
a. Develop and deploy generative/predictive models and maintain high-performance GPU clusters.
Qualifications
a. Degree in Computer Science, Mathematics, or Software Engineering. A Ph.D. is not mandatory.
b. Candidates with a Master’s degree and 3+ years of high-level industry/research experience are preferred. Proficiency in PyTorch/TensorFlow is essential.
Welfare Treatment
a. Competitive salary based on the applicant’s work experience and ability (salary negotiable).
b. Social insurance and housing fund, supplementary medical insurance, physical examination and paid annual leave.
c. Opportunities for career development and available professional guidance.
How to apply
Please send the following materials to fanhao@cimrbj.ac.cn:
1. An updated CV/Resume.
2. A statement of research interests or a future research plan.
3. Highly Recommended: Link to GitHub/code samples or a portfolio of research cases.
4. Contact details for 2–3 professional referees.
Email Subject: [Name] + [Specific Position Applied For].
Contact person: Hao Fan
This recruitment is valid for the long term until a suitable candidate is recruited.
10 Xitoutiao, Youanmen Wai, Fengtai District, Beijing 100069, China
010-86738999
cimr@cimrbj.ac.cn
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