Achieving high-value chemical diversity for the pharmaceutical artificial intelligence
Robotics & Machine Learning
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
"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
Keywords for this news article include: Machine Learning, Emerging Technologies,
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