From High Dimensions to Human Comprehension: Exploring Dimensionality Reduction for Chemical Space Visualization

Dimensionality reduction is an important exploratory data analysis method that allows high-dimensional data to be represented in a human-interpretable lower-dimensional space. It is extensively applied in the analysis of chemical libraries, where chemical structure data — represented as high-dimensional feature vectors—are transformed into 2D or 3D chemical space maps. In this paper, commonly used dimensionality reduction techniques — Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) — are evaluated for exploration of subsets of small molecule organic compounds from ChEMBL database. The performance of these methods is examined in terms of neighborhood preservation and visualization capabilities, and the strengths and limitations are discussed.