MS2toImg: A Framework for Direct Bioactivity Prediction from Raw LC-MS/MS Data

Abstract

Untargeted metabolomics using LC-MS/MS offers the potential to comprehensively profile the chemical diversity of biological samples. However, the process is fundamentally limited by the "identification bottleneck," where only a small fraction of detected features can be annotated using existing spectral libraries, leaving the majority of data uncharacterized and unused. In addition, the inherently low reproducibility of LC-MS/MS instruments introduces alignment errors between runs, making feature alignment across large datasets both error-prone and challenging. To overcome these constraints, we developed a deep learning method that eliminates the requirement for metabolite identification and reduces the influence of alignment inaccuracies. Here, we propose MS2toImg, a method that converts raw LC-MS/MS data into a two-dimensional images representing the global fragmentation pattern of each sample. These images are then used as direct input for a convolutional neural network (CNN), enabling end-to-end prediction of biological activity without explicit feature engineering or alignment. Our approach was validated using wild soybean samples and multiple bioactivity assays (e.g., DPPH, elastase inhibition). The MS2toImg-CNN model outperformed conventional machine learning baselines (e.g., Random Forest, PCA), demonstrating robust classification accuracy across diverse tasks. By transforming raw spectral data into images, our framework is inherently less sensitive to alignment errors caused by low instrument reproducibility, as it leverages the overall fragmentation landscape rather than relying on precise feature matching. This identification-free, image-based approach enables more robust and scalable bioactivity prediction from untargeted metabolomics data, offering a new paradigm for high-throughput functional screening in complex biological systems.