Shirley Siu is an associate professor at the Macau Polytechnic University. She received her PhD in Computational Biology from Saarland University, Germany and was a postdoc at University of Erlangen-Nuremberg. Her research focuses on artificial intelligence drug discovery, biomolecular simulations of proteins and membranes, and the development of molecular force fields. Recent projects include AI-driven discovery of antimicrobial peptides, anticancer peptides, and protein-peptide interactions.  She has authored and co-authored more than 70 journal and indexed-conference papers.

  • Current Employer/Organization
    Macao Polytechnic University
  • Current Position
    Associate Professor

    Macao Polytechnic University

  • Subjects Taught
    • COMP422/COMP2114 Ethics and Professional Issues in Computing  
    • CSAI3121 Machine Learning and Intelligent Data Analysis 

  • Education

    • 2010-2011   Postdoctoral fellow, University of Erlangen-Nuremberg, Germany
    • 2006-2010   PhD in Natural Sciences, Saarland University, Germany
    • 2003-2006   MSc in Computational Molecular Biology, Saarland University, Germany
    • 1998-2001   MSc in Software Engineering, University of Macau
    • 1993-1997   BSc in Software Engineering, University of Macau

  • Research Interests

    • Computer Aided Drug Discovery
    • Molecular Dynamics
    • Cheminformatics
    • Machine Learning
    • Intelligent Optimization Algorithms

  • Work Experience

    • 2023-Present  Associate Professor, Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University
    • 2021-2023  Associate Professor & Research Coordinator, Institute of Science and Environment, University of Saint Joseph, Macau
    • 2012-2021  Assistant Professor, Department of Computer and Information Science, University of Macau
    • 2009-2012  Senior Instructor, Department of Computer and Information Science, University of Macau
    • 2001-2003  Instructor, Department of Computer and Information Science, University of Macau

  • Publications

    For the complete list, please refer to the author profile in Scopus:
    https://www.scopus.com/authid/detail.uri?authorId=57204369713


    2025


    2024


    2023

    • Yan, J.; Zhang, B.; Zhou M.; Campbell-Valois, F-X.; Siu, Shirley W. I.* A deep learning method for predicting the minimum inhibitory concentration of antimicrobial peptides against Escherichia coli using Multi-Branch-CNN and Attention. mSystems. 2023, e0034523. 
    • Qin, Haixin; Zuo, Weimin; Ge, Lilin; Siu, Shirley W. I. ; Wang, Lei; Chen, Xiaoling; Ma, Chengbang; Chen, Tianbao; Zhou, Mei; Cao, Zhijian; Kwok, Hang Fai. Discovery and Analysis of a Novel Antimicrobial Peptide B1AW from the Skin Secretion of Amolops Wuyiensis and Improving the Membrane-binding Affinity through the Construction of the Lysine-introduced Analogue. Computational and Structural Biotechnology Journal 2023. 
    • Lei, Thomas M. T.; Ng, Stanley C. W.; Siu, Shirley W. I.* Application of ANN, XGBoost, and Other ML Methods to Forecast Air Quality in Macau. Sustainability 2023, 15, 5341. 

  • Description

    To date, drug discovery has been a major challenge due to limited knowledge of disease and difficulty in identifying the right molecular targets and drug candidates. Our research focuses on developing computational methods to support drug discovery by innovating in the fields of bioinformatics, machine learning, deep learning, and optimization algorithms. We explore the therapeutic applications of peptides, including antimicrobial, anticancer, GPCR-interacting, and ion channel-interacting peptides. Our work involves modeling the molecular properties of these peptides and correlating them with the biological activities observed in experiments. Additionally, we focus on modeling and simulation of proteins to study their structures, dynamics, and functions. We have been involved in the development of membrane lipid force fields and more recently in the modeling of self-assembling monolayers on biochips. To facilitate virtual screening of drug candidates, we improve molecular docking programs by designing efficient search algorithms. This allows us to better predict the binding interactions between potential drug molecules and their target proteins, accelerating the drug discovery process.


    Our multidisciplinary approach, combining expertise in computational biology, bioinformatics, and machine learning, enables us to tackle the complex challenges in drug discovery and development. Our ultimate goal is to contribute to the identification of novel therapeutic agents and the understanding of disease mechanisms.



    Discover Anticancer Peptides by Screening of Microbe Genomes using Artificial Intelligence

    Anticancer peptides (ACP) open a new and promising avenue for the development of anticancer drugs with higher selectivity and fewer side effects than currently available therapeutics. By harnessing the power of machine learning techniques, we can identify novel ACPs in an accurate and efficient way using a data-driven approach. To facilitate the discovery of novel ACPs from natural sources, we use an in-silico screening workflow which allows us to extract potential ACP sequences from the genomes of organisms known to produce a diverse array of bioactive peptides.


    Our workflow is a multi-step procedure which selects sequences with high anticancer potency and low toxicity. The workflow consists of a ACP classifier and a quantitative activity regressor (xDeep-AcPEP) for six types of tumor cells, including breast, colon, cervix, lung, skin, and prostate. Selected sequences are further filtered by toxicity predictors to generate a list of potential sequences. As a proof-of-concept, the workflow is used to identify novel ACPs for colorectal cancer from the genome sequence of C. albicans. Four candidate ACPs are tested in-vitro and in-vivo, demonstrate anticancer potent activity against colorectal cancer models while exhibiting low toxicity towards normal cells.


    References: