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2017 GTC San Jose

S7282 - GPU-Accelerated Convolutional Neural Networks for Protein-Ligand Scoring

Session Speakers
Session Description

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.


Additional Session Information
Intermediate
Talk
Computational Biology Deep Learning and AI
Healthcare & Life Sciences Higher Education / Research
25 minutes
Session Schedule