AI-Assisted Glycan Structural Elucidation & Comparison

The discovery of complex sugars in wild seal milk, rivalling human milk, highlights the need for advanced tools in glycomics. This challenge focuses on building a data science pipeline to analyze complex mass spectrometry (MS) data for the identification, quantification, and comparative analysis of glycans. Participants will develop a system that can process raw MS/MS data, infer potential glycan structures, and compare glycan profiles across different biological samples (e.g., seal vs. human milk). This involves signal processing, pattern recognition, and leveraging spectral databases. The solution will incorporate advanced machine learning techniques, potentially including graph neural networks or deep learning models, to interpret fragmentation patterns and predict glycan structures. Critical to this challenge is the integration of cutting-edge AI. Participants will leverage the Qwen2.5-72B model to assist in hypothesis generation for complex or novel glycan structures based on ambiguous spectral data, and utilize Groq for high-speed inference of spectral features. Furthermore, a BeeAgent-inspired system will automate the search for known glycan structures in publicly available databases, enhancing the efficiency and accuracy of annotation. This challenge has direct applications in nutrition, drug discovery, and biomarker identification.