Thursday, 22nd of December Baltimore, MD - Scientists at the Pharmaceutical Artificial Intelligence (pharma.AI) group of Insilico Medicine, Inc, today announced the publication of a seminal paper demonstrating the application of generative adversarial autoencoders (AAEs) to generating new molecular fingerprints on demand. The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials.
Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.
GAN is a fresh direction in deep learning invented by Ian Goodfellow in 2014. In recent years GANs produced extraordinary results in generating meaningful images according to the desired descriptions. Similar principles can be applied to drug discovery and biomarker development. This paper represents a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.
"At Insilico Medicine we want to be the supplier of meaningful, high-value drug leads in many disease areas with high probability of passing the Phase I/II clinical trials. While this publication is a proof of concept and only generates the molecular fingerprints with the very basic molecular properties, internally we can now generate entire molecular structures according to a large number of parameters. These structures can be fed into our multi-modal drug discovery pipeline, which predicts therapeutic class, efficacy, side effects and many other parameters. Imagine an intelligent system, which one can instruct to produce a set of molecules with specified properties that kill certain cancer cells at a specified dose in a specific subset of the patient population, then predict the age-adjusted and specific biomarker-adjusted efficacy, predict the adverse effects and evaluate the probability of passing the human clinical trials. This is our big vision", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
Previously, Insilico Medicine demonstrated the predictive power of its discovery systems in the nutraceutical industry. In 2017 Life Extension will launch a range of natural products developed using Insilico Medicine's discovery pipelines. Earlier this year the pharmaceutical artificial intelligence division of Insilico Medicine published several seminal proof of concept papers demonstrating the applications of deep learning to drug discovery, biomarker development and aging research. Recently the authors published a tool in Nature Communications, which is used for dimensionality reduction in transcriptomic data for training deep neural networks (DNNs). The paper published in Molecular Pharmaceutics demonstrating the applications of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data received the American Chemical Society Editors' Choice Award.