Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes

Chemical space visualization supports exploration of high-dimensional molecular data by revealing patterns of similarity, diversity, and structure-property relationships. As chemical libraries expand from thousands to billions of compounds, practical visualization increasingly depends on pipelines that balance chemical meaning with computational and memory constraints. In this survey, we review 56 studies published between 2000 and 2025 and synthesize the end-to-end workflow of chemical space visualization across five core stages: dataset choice, molecular featurization, dimensionality reduction (DR), clustering, and evaluation. We quantify usage trends over time and relate method selection to dataset scale. Across the literature, fingerprints remain the dominant representation for large libraries due to their scalability, while recent studies increasingly incorporate fragments, SMILES-based encodings, and learned embeddings when richer signals are needed. DR practice shows a shift from PCA-centric baselines to neighborhood-preserving methods such as t-SNE and UMAP, with graph layout approaches like TMAP enabling visualization at extreme scale. Clustering shows the weakest convergence to a single standard: hierarchical methods, K-means, and SOM remain frequent choices, complemented by scalable summarization and domain-driven strategies (e.g., BIRCH/BitBIRCH and scaffold-based partitioning) when all-pairs similarity becomes prohibitive. Finally, we propose dataset-size-aware pipeline archetypes and identify open challenges, including inconsistent structure-aware evaluation, limited reproducibility reporting, and the need for scalable, chemically grounded methods for ultra-large libraries.