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

## TL;DR

This paper presents a deep autoencoder-based method for unsupervised clustering of 1.39 million bioactive molecules in 3D chemical space, revealing meaningful sub-structural features and bioactivity patterns.

## Contribution

It introduces a novel deep learning approach for large-scale bioactive molecule clustering that uncovers structural and activity-based subgroups beyond traditional similarity methods.

## Key findings

- Successfully clustered 1.39 million molecules into meaningful band-clusters
- Revealed sub-structural features associated with bioactivity classes
- Demonstrated applicability to big data clustering tasks

## Abstract

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.

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Source: https://tomesphere.com/paper/1902.03429