Dr Meilan Huang leads the computational chemistry and biology group at School of Chemistry and Chemical Engineering at Queen's University Belfast. Her research interest lies in developing and applying Molecular Modelling and Machine Learning methods for transition-metal catalysis, biocatalysis and photocatalysis. The group study the structure-function relationship and the catalytic mechanisms of a range of chemical transformations to guide the rational design of novel catalysts. Dr Huang is a Fellow of Royal Society of Chemistry (FRSC), and also a Fellow of Royal Society of Biology (FRSB).

Before joining Queen's as a lecturer in 2007, Dr Huang worked with Prof Fengling Qing in the Key Laboratory of Organoflorine Chemistry at Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences in 1998-1999. She then pursued PhD in Computational Chemistry at Zhejiang University. After awarded the PhD degree in Feb 2003, she worked as a postdoc with Prof Arvi Rauk in the Department of Chemistry at University of Calgary Canada. She was a Welcome Trust research fellow in the Laboratory of Physical and Theoretical Chemistry at University of Oxford working with Prof W. Graham Richards, in 2004-2006 and a research fellow working with Prof Artem Cherkasov in Department of Medicine at University of British Columbia, Canada in 2006-2007.

Dr Huang has published over 100 full research articles and reviews in high-impact peer-reviewed journals, e.g. Nature Synthesis, 2026, in press. ACS Catal, 2026, 10.1021/acscatal.6c01200, Chem Catalysis, 2026 10.1016/j.checat.2025.101633, J Chem Theo Comp., 2025, 10.1021/acs.jctc.4c01391, Catal Sci Technol 2025, 10.1039/D5CY00502G, Phys Chem Chem Phys, 2025, 10.1039/D4CP03708A, Chem Soc Rev, 2024, 10.1039/d4cs00196f, J Chem Info Model, 2024, J Agri Food Chem, 2024, 10.1021/acs.jafc.4c09515 10.1021/acs.jcim.4c00058, ChemCatChem, 2024, 10.1002/cctc.202400598, Catal Sci Technol. 2023, 10.1039/D3CY00123G,  ACS Catalysis, 2022, 10.1021/acscatal.2c00013;  Inorganic Chemistry, 2021, 10.1021/acs.inorgchem.1c00468; J Phys Chem Lett. 2020, 10.1021/acs.jpclett.0c02105; Chem Comm, 2020, 10.1039/D0CC03721D; Phys Chem Chem Phys. 2020, 10.1039/D0CP03083J; Chem Comm, 2019, 10.1039/C8CC09951K, J Am Chem Soc, 2019, 10.1021/jacs.9b02709, Nature Communications, 2019, 10.1038/s41467-019-11155-3, ACS Catalysis, 2016, 10.1021/acscatal.6b02380,etc.

Dr Huang has led the Computational team in several major interdisciplinary research projects. She was the PI of Chemistry in the interdisciplinary biotechnological project “Development of a computational and molecular biology platform between QUB and Almac” (2015-2019). Dr Huang is the PI of Chemistry in a new 3-year project (2021-2024) INSIGHT@ "IN Silico-Informed metaGenomic Harvesting Technology", in close collaboration with experimentalists and industry. So far, she has secured over £2m research income with a share of £688k. As the principal investigator of the Computational Chemistry workpackage, having supervised 2 computational postdocs. As the principle invesstigator of the Machine Learning workpackage for INSIGHT@, supervising a postdoc, and has developed the Deep-Learning toolkits "ALDELE" (J. Chem. Inf. Model. 2024, 10.1021/acs.jcim.4c00058;) and "BioStructNet" (J. Chem. Theory Comput. 2025 https://doi.org/10.1021/acs.jctc.4c01391) for predicting functions of biocatalysts. 

Dr Huang is the PI and Director of the new BBSRC-funded Doctoral Training Programe BioAID: AI-Driven Enzyme Design for Industrial Biocatalysis:

UKRI-Doctoral-Training-Investment-Set-to-Power-UK-Growth

Computational Chemistry and Biology;

Theoretical Biocatalysis;

Machine Learning and Statistical Potential;

Rational Molecular Design;

Photocatalytis;

Electric Field in Biocatalysis

Current teaching commitments:

CCE Summer School Programme (Programme Lead)

CHM3016: Computational Chemistry in Drug Discovery 

CHM4003: Advanced Physical Chemistry (Module Coordinator)

CHM3010: Practical Skills in Chemistry (Module Coordinator)

CHM2007: Drug Development

CHM4001: Chemistry Research Project

CHM7004: Research Project

2021-2024: "INSIGHT: IN Silico Informed metaGenomic Harvesting Technology platform - Development of an advanced and secure enzyme discovery platform for Almac and QUB" Invest NI, RD11181114, £1,299,329. PI of Chemistry

2021-2023: Royal Society: IEC\NSFC\201177 - International Exchanges 2020 “Machine Learning-assisted Directed Evolution of Enzymes”. PI

2021: Newton Fund Research Links Workshop grant-2020-RLWK12-10149 "Catalytic Chemistry and Chemical Technology of C1 process" British Council. PI

2015-2019: "New Biotechnology: Development of Computational and Molecular Biology Platforms for Almac and QUB" Invest NI, RD3014092, £981,360.  PI of Chemistry

2022: Innovation in Teaching Fund: VR in Chemistry Lab

2017: Queen's Student Union "Education Excellence Award"