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GTC ON-DEMAND

Computational Biology & Chemistry
Presentation
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Molecular Generative VAEs: Parallelization, Optimization, and Latent Space Analysis on the DGX-1
Abstract:
Generative Variational Autoencoders (VAE) in molecular discovery and new materials design have recently gained considerable attention in academia as well as industry (Gomez-Bombarelli, 2017). In this talk, we will present results from a combined Dow Chemical and NVIDIA development effort to implement a VAE for chemical discovery. We'll discuss challenges associated with applying deep learning to chemistry and highlight recently developed methods. Highlights from our presentation will include a discussion of methods to analyze and sample from an organized latent representation in a conditioned variational autoencoder, tips for training a complex architecture, distributed multi-node training using Horovod, and results showing the generation of molecular structure with associated property prediction.
 
Topics:
Computational Biology & Chemistry, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9417
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