Molecules imagined using next-generation artificial intelligence validated experimentally

Tuesday, September 11th, Rockville, MD - Today, Insilico Medicine, Inc., a Rockville-based next-generation artificial intelligence company specializing in the application of deep learning for target identification, drug discovery and aging research announces the publication of a new research paper "Entangled Conditional Adversarial Autoencoder for de-novo Drug Discovery" in Molecular Pharmaceutics, the leading American Chemical Society journal covering research on the molecular mechanistic understanding of drug delivery and drug delivery systems. The authors presented an original deep neural network architecture, Entangled Conditional Adversarial Autoencoder (ECAAE), which generates molecular structures based on various properties such as activity against a specific protein, solubility, and ease of synthesis. ECAAE was used to generate a novel inhibitor of Janus Kinase 3 (JAK3), implicated in rheumatoid arthritis, psoriasis, and vitiligo. The discovered molecule was tested in vitro and demonstrated high activity and selectivity. Generative Adversarial Networks (GANs) proposed by Ian Goodfellow and colleagues in 2014 and commonly referred to as AI imagination, are among the most exciting areas of AI research.