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2018 GTC San Jose

S8540 - Deep Learning for Molecular Docking

Session Speakers
Session Description

Molecular docking is an important tool for computational drug discovery that aims to predict the binding pose of a ligand (drug) to a target protein. Identifying a correctly oriented pose requires a scoring function that has a global optimum close to the experimentally observed pose. Additionally, it should also be differentiable with respect to atomic positions so that it can be used for gradient-based pose optimization. We'll describe a differentiable grid-based convolutional neural network scoring function and explore its application in an end-to-end GPU-optimized molecular docking workflow. We'll show that convolutional neural networks trained on experimental data can successfully identify correct binding modes and meaningfully rank and score compounds. We'll also describe several visualization approaches that map the CNN score back to the atomic inputs to help guide medicinal chemistry optimization and provide insight into the functioning of the neural network. The entirety of our approach is available under an open-source license as part of our gnina package (https://github.com/gnina).


Additional Information
Computational Biology / Chemistry Deep Learning and AI Frameworks
Healthcare & Life Sciences
All technical
Talk
25 minutes
Session Schedule