AIDDPM Lab — AI for Drug Discovery & Precision Medicine

Research Directions

What We Work On

AIDDPM Lab sits at the intersection of artificial intelligence, computational chemistry, and biomedical science. Our three core research pillars drive discoveries from molecular design to clinical translation.

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AI-Driven Drug Design & Discovery

Deep learning models for molecular property prediction, lead optimization, and closed-loop automated drug design. Key tools: MolMap, Leadmaster, and contrastive learning for activity cliff overcoming.

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Precision Cancer Immunotherapy

Building predictive models for immunotherapy response across cancer types. Our COMPASS model achieves multi-center clinical validation and drives clinical translation of AI in precision oncology.

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Cancer Multi-Omics AI

Integrating genomics, transcriptomics, and proteomics through structured representation learning. Key tools: AggMap for feature-aggregated multi-channel networks.

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Foundation Models for Biomedicine

Exploring large-scale pre-trained models and agents (e.g., Medea omics AI agent) for therapeutic discovery, leveraging graph neural networks, contrastive learning, and multi-modal fusion.

Lab Updates

News & Highlights

2026.01 Preprint Medea: An omics AI agent for therapeutic discovery published on openRxiv (Sui, Shen et al.).
2025.05 Preprint New preprint: "Generalizable AI predicts immunotherapy outcomes across cancers and treatments" — Shen, Nguyen, Zitnik et al.
2025.09 Welcome Dr. Wanxiang Shen officially joins Zhejiang University College of Pharmaceutical Sciences as an Assistant Professor (ZJU Hundred Talents Program).
2024.11 Paper PocketGen published in Nature Machine Intelligence (Zhang, Shen, Liu, Zitnik).
2024.11 Award Dr. Shen selected for the National High-Level Overseas Young Talent Program (国家级海外高层次青年人才项目).
2024.06 Paper Bidirectional DNA barcoding for protein interactions published in Nature Biomedical Engineering.