Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning
This addresses the challenge of mapping chemical space for drug discovery, though it appears incremental as it applies deep autoencoders to an existing task.
The paper tackled the problem of clustering bioactive molecules in 3D chemical space using unsupervised deep learning, resulting in the clustering of 1.39 million molecules into band-clusters that group molecules by bioactivity classes and reveal common sub-structural features.
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.