  Unsupervised clustering has broad applications in data stratification,
pattern investigation and new discovery beyond existing knowledge. In
particular, clustering of bioactive molecules facilitates chemical space
mapping, structure-activity studies, and drug discovery. These tasks,
conventionally conducted by similarity-based methods, are complicated by data
complexity and diversity. We ex-plored the superior learning capability of deep
autoencoders for unsupervised clustering of 1.39 mil-lion bioactive molecules
into band-clusters in a 3-dimensional latent chemical space. These
band-clusters, displayed by a space-navigation simulation software, band
molecules of selected bioactivity classes into individual band-clusters
possessing unique sets of common sub-structural features beyond structural
similarity. These sub-structural features form the frameworks of the
literature-reported pharmacophores and privileged fragments. Within each
band-cluster, molecules are further banded into selected sub-regions with
respect to their bioactivity target, sub-structural features and molecular
scaffolds. Our method is potentially applicable for big data clustering tasks
of different fields.
