Author: shirleysiu
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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 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： 肽生物活性预测、发现与设计开发机器学习和深度学习方法，预测和设计具有治疗潜力的肽类药物——抗菌、抗癌、抗氧化、毒素和抗毒素。 靶向特异性肽生成模型推进定制化生成式人工智能模型，用于设计靶向特定蛋白质和疾病通路的肽类药物。 通过人工智能驱动的药物发现 (AIDD) 和计算机辅助药物设计 (CADD) 发现小分子抑制剂应用人工智能和计算化学工具识别和优化新型小分子疗法。 大型语言模型和代理生物医学数据管理利用文献挖掘和自然语言处理，自动提取和管理生物医学知识，加速发现疾病的分子机制及药物再利用。 使用综合计算方法研究与疾病相关的分子机制，特别是微生物耐药性、癌症进展和衰老。 资格： 具备严谨的科学态度、团队合作精神、良好的沟通能力、积极乐观的态度 拥有计算化学/生物学/物理学、生物信息学或密切相关领域的硕士学位 也欢迎具有药学和生命科学背景、具备肽合成和实验技能、并且热衷于学习人工智能的学生！ 熟悉AI编程 具有分子建模或模拟经验優先 良好的英语口语和写作能力 全额奖学金澳门币20,000元/月，上限3年 半额奖学金澳门币10,000元/月，上限3年 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： PhD programme in AI Driven Drug discovery Key dates 学费 University admission page 內地考生專頁 Academic…
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PhD Positions in AI Driven Drug Discovery (2026/2027 intake)
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…
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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…
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Master thesis projects
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…
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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…
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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…
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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…
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Xinke’s work on DTI prediction is now published in Digital Discovery
Congratulations to Xinke Zhan on the publication of his new work in Digital Discovery! The innovative multi-perspective attention approach of the MAARDTI model offers a powerful advance for the prediction of drug-target interactions. We look forward to its impact on drug discovery! We are especially grateful for the many constructive…
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The Team Publishes New Progress in Chemical Ecotoxicological Prediction in ACS Omega
Congrats to Xinpo on his ACS Omega paper published on Aug 12 comparing machine learning and deep graph learning for chemical ecotoxicity prediction. The study highlights the superior performance of GCN for environmental safety modeling, especially for same-species predictions. However, cross-species prediction remains challenging. Immediate testing of chemicals is available…
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Screening of novel anticancer peptides from bacterial genome
Our work on the discovery of novel anticancer peptides using AI approaches is published in the Journal of Chemical Information and Modeling. This is a collaborative work with the team of Henry Kwok, Chris Wong, and Lawrence Si from University of Macau. In this research, we developed an AI workflow…
