Insilico Medicine announces the publication of a new research paper in Molecular Pharmaceutics titled: “druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.”
Modern Generative Adversarial Networks (GAN)s achieved unprecedented accuracy and quality in image, video and text generation. The fundamental principle of GANs is adversarial training based on game theory results. The competition between the Generative and Discriminative networks leads to joint evolution and almost perfect results.
One of the most significant tasks at Insilico Medicine is adapting best neural network architectures for drug discovery process and it is committed to publishing the proof of concept advances that are at least one year old. These advances are usually integrated into a comprehensive drug discovery pipeline with the goal to enable the deep neural networks to produce perfect molecules for the specific set of diseases.
DruGAN allows the generation of new formulations for a wide range of diseases: different cancers, neurodegenerative diseases such as Alzheimer’s disease, virus infections, and more. Of course, DruGAN is not a silver bullet and for successful usage; it requires a large team of professionals in both AI and medicinal chemistry.
One of the limitations of the published approach is the use of the binary molecular fingerprints and the need to match the output molecules to the chemical libraries. To overcome these barriers, Insilico Medicine transitioned to novel representations of molecular structure based on the molecular graphs and presented the work at its annual “Artificial Intelligence and Blockchain for Healthcare” forum in Basel, Switzerland in September.
“Insilico Medicine has a policy of publishing the proof of concept research, which is one year or older to attract more data scientists to work on the healthcare problems. DruGAN is one of these proofs of concept. Internally the company switched to GANs with reinforcement learning (RL), which is essentially the environment that rewards GANs for generating “effective” novel molecular graphs.
The molecules discovered using these techniques went through the in vitro validation and are undergoing in vivo testing. The use of GANs with RL is likely to transform the pharmaceutical industry”, said Alex Zhavoronkov, Ph.D., founder and CEO of Insilico Medicine, Inc.
(Source: EurekAlert!)
