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GTC ON-DEMAND

Computational Biology & Chemistry
Presentation
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GPU-Accelerated Convolutional Neural Networks for Protein-Ligand Scoring
Abstract:
We'll describe a convolutional neural network that takes as input a comprehensive 3D representation of a protein-ligand interaction and predicts whether the ligand (a small molecule, like a drug) binds to the protein. We'll provide a brief orientation in structure-based drug design, describe how we effectively use the GPU to efficiently train, evaluate, and visualize our neural networks, and discuss preliminary results and current limitations. Our CNN scoring function outperforms the conventional AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.
 
Topics:
Computational Biology & Chemistry, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7282
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