MultiT2: A Tool Connecting the Multimodal Data for Bacterial Aromatic Polyketide Natural Products

The integration of artificial intelligence (AI) into natural product science is an exciting and rapidly evolving area of research. By combining classical chemistry and biology with deep learning, these technologies have significantly improved research efficiency, particularly in overcoming laborious and time-consuming processes. Recently, there has been growing interest in leveraging multimodal algorithms to integrate biologically relevant yet mathematically disparate data sets in order to reorganize knowledge graphs. However, to the best of our knowledge, no studies have yet applied this approach specifically within the natural product field. This is largely because correlating multimodal natural product data is challenging due to their high degree of fragmentation. Here, we present MultiT2, an algorithm that connects these disparate data from bacterial aromatic polyketides, which form a medically important natural product family, as a showcase. Through a large-scale causal inference process, this approach aims to transcend mere prediction, unlocking new dimensions in the natural product discovery and research domains.