Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

2025-02-21

AI Summary

This study addresses critical challenges in AI-driven proteomics—namely, inaccurate peptide/protein identification and quantification, limited analytical capability for spatial and perturbation proteomics, weak multi-omics integration, and the non-computability of virtual cells. To overcome these, we propose a comprehensive “AI-ready proteomics ecosystem” framework spanning data generation, sharing, and modeling. Our methodology integrates deep learning, graph neural networks, multimodal representation learning, and computational mass spectrometry modeling, synergized with standardized high-throughput MS workflows, spatial imaging, and systematic perturbation experimental design. We distill seven strategic research directions and formulate a technically feasible roadmap. Crucially, this work achieves the first computationally tractable implementation of the virtual cell concept—transitioning it from theoretical abstraction to an executable paradigm. The resulting framework establishes an internationally aligned methodology and implementation guide for AI-powered precision proteomics.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

Problem

• AI enhances proteomics data analysis
• AI improves protein identification and quantification
• AI integrates multi-omics for virtual cells

Innovation

• AI enhances proteomics data analysis
• AI improves protein identification accuracy
• AI integrates multi-omics for virtual cells