χ-SOM: emergent self-organizing maps for large chemical space visualization

Abstract The exploration and visualization of chemical space using dimensionality reduction (DR) methods are fundamental to cheminformatics, as they enable the exploitation of the similar property principle. However, most methods, including t-SNE and UMAP, struggle to preserve valuable distance and density properties from ambient space in the latent space representation. The emergent self-organizing map (ESOM), an often-ignored extension of the self-organizing map (SOM) algorithm, demonstrates projection behavior that circumvents the disadvantages of these methods. As a suitable implementation of this algorithm for large cheminformatic datasets is not available, we present χ-SOM, the ChemInformatics (ChI)-SOM library, a high-performance framework for training emergent self-organizing maps on large molecular datasets and for interactively exploring the resulting projections. χ-SOM integrates an optimized implementation of the SOM algorithm with CPU and CUDA backends, an on-disk HDF5-based datastore with PyTorch DataLoader support for efficient parallel access to millions of fingerprints, and a graphical user interface tailored to chemical structure visualization and property overlays. We compared our implementation with t-SNE and UMAP on synthetic and chemical data benchmarks that probe density and inter‑cluster distance preservation. On the synthetic benchmarks, ESOM retained both properties where t‑SNE and UMAP did not. On the fingerprint-based equivalents, all three methods performed comparably. χ-SOM was also benchmarked against established SOM libraries across varying dataset sizes and fingerprint lengths, demonstrating competitive or superior performance. As a final proof-of-concept, we successfully trained an ESOM of the ChEMBL33 database (1.7 million compounds; 5.1 million neurons), to our knowledge the largest SOM reported to date, and show that local neighborhood relationships on the map align well with medicinal chemists’ intuition of chemical similarity. χ-SOM thus provides a practical, extensible tool for large scale exploratory analysis of chemical space.