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GTC ON-DEMAND

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Abstract:
Generative modeling is a new paradigm for molecular design that translates the growing amount of biomolecular data into an efficient method of predicting novel drug candidate molecules called ligands. We've applied convolutional neural networks to a space-filling atomic density representation of molecular structures to perform protein-ligand scoring and molecular docking. We'll describe our recent work with 3D generative models of molecular structure that generate ligand atom densities conditioned on a receptor-binding site. Our dual-encoding network architecture allows us to interpolate in the protein space and the ligand space. We'll show that our models successfully generate ligand densities conditional on a given receptor, discuss the challenges in mapping a continuous latent space to discrete chemical space, and explain our approaches to latent space regularization.
Generative modeling is a new paradigm for molecular design that translates the growing amount of biomolecular data into an efficient method of predicting novel drug candidate molecules called ligands. We've applied convolutional neural networks to a space-filling atomic density representation of molecular structures to perform protein-ligand scoring and molecular docking. We'll describe our recent work with 3D generative models of molecular structure that generate ligand atom densities conditioned on a receptor-binding site. Our dual-encoding network architecture allows us to interpolate in the protein space and the ligand space. We'll show that our models successfully generate ligand densities conditional on a given receptor, discuss the challenges in mapping a continuous latent space to discrete chemical space, and explain our approaches to latent space regularization.  Back
 
Topics:
Computational Biology & Chemistry, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9699
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Abstract:
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).
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).  Back
 
Topics:
Computational Biology & Chemistry, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8540
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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.
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.  Back
 
Topics:
Computational Biology & Chemistry, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7282
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