Welcome to CBBio@MPU! We are dedicated to advancing computational biology to deepen our understanding of biomolecular systems and to enable discovery across the health and life sciences. By integrating biological insight with AI-driven methods, we address challenging questions at the interface of computation and biology.
In drug discovery, our research spans method development for peptide and small-molecule drug discovery and property prediction, from target identification, lead discovery to (bio)synthesis prediction. Current projects focus on antimicrobial peptides (AMPs), anticancer peptides (ACPs), antioxidant peptides (AOPs), bacterial targets (TxSS), and cancer targets (TROP2 and HER2).
In health sciences, we work on medical imaging diagnosis, with a particular focus on infectious diseases, as well as spatial gene expression prediction from histopathological images for cancer research.
Keywords: 人工智能藥物與多肽發現；蛋白質語言模型；生物醫學知識圖譜；生物催化/計算酶學；分子建模與化學資訊學；醫學影像分析. AI-Driven Drug and Peptide Discovery; Protein Language Models; Biomedical Knowledge Graphs; Biocatalysis / Computational Enzymology; Molecular Modeling and Cheminformatics; Medical Image Analysis
Explore our app portal (https://app.cbbio.online). Some program source codes can be downloaded from our GitHub page or SourceForge page.
Join us!!! PhD admission 2026/2027 (Admission period: 15 Oct 2025 to 15 May 2026) 「中文版」
我们現正招募 2026/2027 年秋季入学的博士生。申请受理直至 2027年 5月15日。 Shirley Siu 澳门理工大学（MPU）人工智能驱动药物发现中心（AIDD）的团队正在招收2026/2027年秋季入学的博士生。我们是一支充满活力的研究团队，致力于人工智能和药物发现交叉领域的前沿研究。我们开发并应用人工智能方法和计算化学技术来解决药物发现过程中的复杂问题。硏究项目包括靶标识别、蛋白质-配体和蛋白质-肽结合预测、虚拟筛选以及生物分子和化合物的活性和功能预测。 我们诚邀充满热情和动力的博士生参与以下创新项目： 资格： 期间：通常为 3-4 年 课程将在澳门及横琴校区上课。 申请截止：招生采取滚动录取政策，最后申请日期為2027年5月15日（参见招生网站） 询问：有意者请电郵 shirleysiu@mpu.edu.mo（请提供意向书、简历、成绩单和代表性出版物） 课程和入学信息：
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We are admitting new PhDs for the 2026/2027 cohort. Official application is until 15 May 2027. 🚀 PhD Recruitment: Computational Biology & AI-Driven Drug Discovery 🚀 Join our research team exploring the intersection of AI, computational biology, and drug discovery. We invite passionate and motivated PhD candidates to work on innovative projects including: Application: Applications…
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Protein-peptide interactions are central to many biological processes, but the scarcity of experimental data makes their prediction challenging. In our latest work, Xinke Zhan led the development of PepInter, a deep learning framework that leverages pretrained protein language models and large-scale structural pretraining to learn interaction-aware representations, achieving strong results across multiple benchmarks. We’re excited…
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Antimicrobial Peptide Prediction Server
Discover the antimicrobial potential of your peptides, perform genome screening to identify novel AMPs.
Anticancer Peptide Prediction Server
Discover the anticancer potential of your peptides on six major types of cancers!
What’s next?
Want to be a designer of the next AI generation tools for therapeutics peptides, come and join us!
Chemical-Protein Target Prediction
Which proteins your ligand might bind with? Search with our AI tools.
Simple Chemical Toxicity Prediction
Our first regression model of the chemical toxicity prediction for acute oral toxicity.
Advanced Chemical Toxicity Prediction
Toxicity prediction against 12 endpoints, 7 nuclear receptor signals and 5 stress response indicators.
Accelerating drug discoveries through AI and molecular simulations
Research | Publications | Software | Team
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AIDD@Macao Polytechnic University
R. de Luís Gonzaga Gomes, Macao, China
