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Unsupervised clustering has broad applications in data stratification,pattern investigation and new discovery beyond existing knowledge. Inparticular, clustering of bioactive molecules facilitates chemical spacemapping, structure-activity studies, and drug discovery. These tasks,conventionally conducted by similarity-based methods, are complicated by datacomplexity and diversity. We ex-plored the superior learning capability of deepautoencoders for unsupervised clustering of 1.39 mil-lion bioactive moleculesinto band-clusters in a 3-dimensional latent chemical space. Theseband-clusters, displayed by a space-navigation simulation software, bandmolecules of selected bioactivity classes into individual band-clusterspossessing unique sets of common sub-structural features beyond structuralsimilarity. These sub-structural features form the frameworks of theliterature-reported pharmacophores and privileged fragments. Within eachband-cluster, molecules are further banded into selected sub-regions withrespect to their bioactivity target, sub-structural features and molecularscaffolds. Our method is potentially applicable for big data clustering tasksof different fields.

Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning