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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

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.

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