Graphical Representation of GANs Making New Molecules

Thursday, May 10, 2018, Baltimore, MD - Insilico Medicine, a Baltimore-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 in Molecular Pharmaceutics journal titled "Adversarial Threshold Neural Computer for Molecular De Novo Design". The described Adversarial Threshold Neural Computer (ATNC) model based on the combination of Generative Adversarial Networks (GANs) with Reinforcement Learning (RL) is intended for the design of novel small organic molecules with the desired set of pharmacological properties. "This is a proof of concept scratching the surface of what we have in house. Stay tuned for the cool experimental validation results to be announced this Summer. I hope that part of this work integrated into our pipeline will help make the world a better and healthier place and help make perfect molecules for specific targets and multiple targets that will have a much higher chance of becoming great drugs", said Evgeny Putin, the deep learning lead at Insilico Medicine. The architecture of GANs was initially proposed by Ian Goodfellow in 2015, and since the inception, the GAN-based models have achieved the unprecedented accuracy in image, video and text generation.