OUR MISSION is to improve computational methods for accelerating the drug discovery and development process. We have been developing bioinformatics tools that enable researchers to solve critical drug discovery problems including protein-ligand binding (PSOVina, GWOVina), drug target identification (LigTMap), biological activity (AxPEP, AcPEP) and toxicity predictions (BESTox, SSL-GCN). Our previously developed methods can be accessed directly online via this apps portal (https://app.cbbio.online). Some program source codes can be downloaded from our GitHub page or SourceForge page.

Notice

Are you a bachelor’s, master’s, or PhD student interested in cutting-edge research in computational biology, bioinformatics, and drug discovery? We welcome inquiries from motivated students who would like to join our team and contribute to our exciting projects. Please send an email to Shirley Siu.

PhD admission 2026/2027 (Admission period: 15 Oct 2025 to 15 May 2026)

招收2026年秋季入學博士生（正式申请于 2025 年 10 月 15开始，2026 年5月15結束。）

Guested-edited issue: Calling for submission

Participating journals:

Frontiers in Bioinformatics, Drug Discovery, Mircrobiology, Cellular and Infection Microbiology

What’s new?

News

Xiangyu’s work on spatial gene expression prediction is now published in Engineering Applications of Artificial Intelligence

Congratulations to Xiangyu on the publication of the new work in Engineering Applications of Artificial Intelligence! In this paper, the team proposes CrossToGene, a bidirectional cross-modality interaction framework for predicting spatial gene expression from histopathological images. The method introduces spatial encoding to capture both macro/micro tissue cues, a bidirectional cross-modal interaction module for stronger fusion…

Xinpo’s Recent Studies on Drug–Target Interaction and Molecular Modeling Are Now Published in the Journal of Chemical Information and Modeling and the Journal of Cheminformatics

Congratulations to Xinpo Lou on the publication of his two recent works in Journal of Chemical Information and Modeling and Journal of Cheminformatics! The proposed MambaTransDTA model introduces a novel hybrid Mamba–Transformer architecture for drug–target binding affinity prediction. By effectively combining long-range dependency modeling with local interaction learning, the model achieves strong and consistent performance…

澳门理工大学招收人工智能药物发现博士生

我们現正招募 2026/2027 年秋季入学的博士生。申请受理直至 2026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2025/2026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 年课程将于2026/2027学年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2026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：

Online Peptide Prediction Tools

Antimicrobial Peptide Prediction Server

Discover the antimicrobial potential of your peptides, perform genome screening to identify novel AMPs.

If you need to access the old server, please use this link.

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

Research | Publications | Software | Team

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AIDD@Macao Polytechnic University

R. de Luís Gonzaga Gomes, Macao, China