Activity Number:
|
241
- SLDS CPapers New
|
Type:
|
Contributed
|
Date/Time:
|
Monday, July 30, 2018 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Science
|
Abstract #328957
|
Presentation
|
Title:
|
Deep Neural Network Model for Predicting Gene Activity Using Three-Dimensional Structures of Chemical Compounds
|
Author(s):
|
Pingzhao Hu* and Md. Mohaiminul Islam and Kevin Jeffers and Andrew M Hogan and Rebecca Davis and Silvia Cardona
|
Companies:
|
University of Manitoba and University of Manitoba and University of Manitoba and University of Manitoba and University of Manitoba and University of Manitoba
|
Keywords:
|
deep neural network;
three-dimensional structure;
chemical compounds;
drug discovery;
gene activity
|
Abstract:
|
Experimental approaches to drug discovery are time-consuming and expensive. It is well-known that three-dimensional (3D) structures of chemical compounds contain rich information for drug screening. Therefore, it is critical to develop new models to measure compound structure-activity relationships. To solve this issue, we first developed an algorithm to extract compound structure-specific features from atomic coordinates of conformers created on a specific molecular conformation. A denoising stacked autoencoder model was then proposed to generate deep features. The network was built by stacking layers of denoising autoencoders in a convolutional way. Chemogenetic interactions were then predicted using a support vector machine based on the learned high-level feature representations of the 3D structures of the compounds. The models were evaluated using 59 compounds with 6413 conformers and 242 gene products generated by a chemical genomics strategy for mechanism-based profiling of antibacterial compounds. We demonstrated that the proposed model has excellent performance to classify chemogenetic interactions using the structure features extracted from the chemical compounds.
|
Authors who are presenting talks have a * after their name.