Centre for AI Driven Drug Discovery at Macao Polytechnic University
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
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…
We are looking for motivated Master’s students to join us for the following master thesis projects: AI-Driven Smart Monitoring and Disease Management System for Cowpea Cultivation more will be posted 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…
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…
