AtomNet® Technology Helps Discover Novel Inhibitors Using Only A Homology Model

July 30, 2020
Events, ACS2020

At the American Chemical Society Fall 2020 Virtual Meeting & Expo, several Atomwise members and partners were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Fall 2020 Virtual Meeting & Expo for other presentation sessions. 

AS (1)Adrian Stecula, PhD 

Co-Author: Muhammad S. Hussain 
Dept. of Chemistry and Biochemistry, University of Toledo 

Corresponding Author: Ronald E. Viola 
Dept. of Chemistry and Biochemistry, University of Toledo

Title: Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a Deep Convolutional Neural Network

Division: MEDI



Rare neglected diseases may be neglected, but are hardly rare, affecting hundreds of millions of people around the world. Here, we present a hit identification approach using AtomNet®, the world’s first deep convolutional neural network for structure-based drug discovery, to identify inhibitors targeting aspartate N-acetyltransferase (ANAT), a promising target for the treatment of patients suffering from Canavan disease. Despite the lack of a protein structure or high sequence identity homologous templates, the approach successfully identified five low-micromolar inhibitors with drug-like properties.


Poster Presentation

On-demand Poster (click for hi-resolution)


On-demand audio recording


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