Could deep-learning systems radically transform drug discovery?

AI drug-discovery engine to be presented at Machine Intelligence Summit in Berlin on June 29-30
June 17, 2016

(credit: Insilico Medicine)

Scientists at Insilico Medicine have developed a new drug-discovery engine that they say is capable of predicting therapeutic use, toxicity, and adverse effects of thousands of molecules, and they plan to reveal it at the Re-Work Machine Intelligence Summit in Berlin, June 29–30.

Drug discovery takes decades, with high failure rates. Among the reasons: irreproducible experiments with poor choice of animal models and inability to translate the results from animal models directly to humans, the wide variety of diseases, and communication difficulties between scientists, managers, venture capitalists, pharmaceutical companies and regulators. And perhaps the biggest problem: the slow-paced, bureaucratic culture in the pharmaceutical industry, the researchers note.

Radically transforming pharmas with AI

Insilico Medicine says it aims to address these reasons by developing “multimodal deep-learned and parametric biomarkers,” as well as multiple drug-scoring pipelines for drug discovery and drug repurposing, and hypothesis and lead generation.

“At Insilico, we want to radically transform the pharmaceutical industry and double the number of drugs on the market, using artificial intelligence and deep understanding of pharmaceutical R&D processes,” said Polina Mamoshina*, senior research scientist at Insilico Medicine, Inc.

“We decided to start with nutraceuticals and cosmetics, but soon we will be announcing our cancer immunology concomitant drug discovery engine to boost the response rates to checkpoint inhibitors in immuno-oncology.”

“Using our drug discovery engine, we made thousands of hypotheses and narrowed these down to 800 strong molecule-disease predictions, with efficacy, toxicity, adverse effects, bioavailability and many other parameters,”┬ásaid Alex Aliper, president of Insilico Medicine, Inc.

“We added many drug scoring mechanisms that further validate the initial predictions and put together a team of analysts to research and evaluate individual molecules. We are now partnering with various institutions to validate these predictions in vitro and in vivo.”

As KurzweilAI reported, earlier this month, Insilico Medicine signed an exclusive agreement with Life Extension, a major nutraceutical product vendor, to collaboratively develop a set of geroprotectors — natural products that mimic the healthy young state in multiple old tissues. The goal is to increase the rejuvenation rate of the body and slow down, or even reverse, the aging process.

Polina Mamoshina was the lead author on the paper, “Applications of Deep Learning in Biomedicine” in Molecular Pharmaceutics and contributed to another publication, “Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data” also in Molecular Pharmaceutics. The later paper received the Editors’ Choice Award from the American Chemical Society. She also co-authored a paper, “Deep biomarkers of human aging: Application of deep neural networks to biomarker development” in Aging, one of the highest-impact journals in aging research.