# TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

## TL;DR

TopologyNet leverages element specific persistent homology to encode 3D biomolecular structures into topological features, enabling deep learning models to accurately predict protein-ligand binding and mutation effects despite limited data.

## Contribution

The paper introduces a novel topological representation (ESPH) combined with CNNs, including a multitask variant, to improve biomolecular property predictions over existing methods.

## Key findings

- Outperforms state-of-the-art in binding affinity prediction
- Accurately predicts mutation impacts on protein stability
- Effective with small and noisy datasets

## Abstract

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.