Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Abstract
Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific...
Paper Details
Title
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Published Date
Sep 4, 2018
Journal
Volume
15
Issue
10
Pages
4398 - 4405
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