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.

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」

Latest News

News

Advancing AI for Protein-Peptide Interaction Prediction

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…

Master thesis projects

We are looking for motivated Master’s students to join us for the following master thesis projects: Contact: Please contact Prof. Shirley Siu by email or pay a visit to the Academic Building E710-4 @ MPU. 2026/2027 AI-Driven Smart Monitoring and Disease Management System for Cowpea Cultivation Background and Motivation: The cowpea plant (Vigna unguiculata) is…

Congratulations to Cai on her successful PhD defense!

We are proud to share that Jianxiu Cai successfully defended her dissertation on Thursday (Apr 23, 2026). Her work, “Computational Modeling of Peptide Bioactivities Using Sequence-based Deep Learning Architecture”, designed both large and slim deep learning models to address the challenges of peptide activity prediction, generating highly accurate models despite small datasets, high structural flexibility…

Online Peptide Prediction Tools

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!

Online Small Molecular Chemical Prediction Tools

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.

Computational Biology and Bioinformatics

Accelerating drug discoveries through AI and molecular simulations

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AIDD@Macao Polytechnic University

R. de Luís Gonzaga Gomes, Macao, China