Deep learning system for drug discovery to be presented at the Machine Intelligence Summit in Berlin
June 14, 2016, Baltimore, MD, Following the publication of the first proof of concept of predicting the functional properties of drugs by their transcriptional response signature, scientists at Insilico Medicine developed a multimodal input drug discovery engine capable of predicting therapeutic use, toxicity and adverse effects of thousands of molecules. Several of these advances will be presented at the Re-Work Machine Intelligence Summit Berlin, June 29-30.
Drug discovery processes within the pharmaceutical and even biotechnology companies is generally very slow, takes decades and usually results in failures with less than 1 in 10 drugs in clinical trials reaching the market. There are many reasons for high failure rates: irreproducible experiments published in top peer-reviewed journals, poor choice of animal models or inability to translate the results from animal models directly to humans, high heterogeneity of diseases and heterogeneity of the patient population and asymmetry of information among scientists, managers, venture capitalists, pharmaceutical companies and regulators. Another reason is the slow-paced and bureaucratic culture within the pharmaceutical companies. Insilico Medicine aims to address all of these reasons by developing multimodal deep learned and parametric biomarkers as well as multiple drug scoring pipelines for drug discovery and drug repurposing, hypothesis and lead generation. Many leading company scientists are hired through exhaustive hackathons and competitions. One of these scientists is Polina Mamoshina, senior research scientist at the Pharmaceutical Artificial Intelligence division of Insilico Medicine.
"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 the pharmaceutical R&D processes. 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", said Polina Mamoshina, senior research scientist at Insilico Medicine, Inc.
Earlier this month Insilico Medicine signed an exclusive agreement with Life Extension, a major nutraceutical product vendor to collaboratively develop set of geroprotectors, natural products that mimic the healthy young state in multiple old tissues. This products are able to increase rejuvenation rate of human body and slow down or even reverse aging process.
Polina Mamoshina was the lead author on 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.
"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. 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", said Alex Aliper, president of Insilico Medicine, Inc.
Insilico Medicine previously presented the deep learned biomarkers of aging at the Re-Work Deep Learning in Healthcare conference in London before the publication of the research paper.
"We really like Re-Work conferences as they bring together machine intelligence experts from many industries and focus on applications and benefits of various approaches. Previously we managed to meet both research and industry partners. At Insilico, we are trying to encourage women in science, especially young female scientists. This makes events run by all-female Re-Work team special for us. I highly recommend anyone interested in practical applications of AI to attend their events. ", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
Machine Intelligence Summit Berlin conference will transpire 29 – 30 of June 2016 at a unique church turned conference venue that is more than 100 years old located at Umweltforum, Pufendorfstr. 11, 10249 Berlin. https://www.re-work.co/events/machine-intelligence-berlin-2016
About Insilico Medicine
Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland hiring talent through hackathons and competitions. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8
RE* WORK is an all-female run events organising company that brings together breakthrough technology, cutting-edge science and entrepreneurship shaping the future of business and society. At each event, it showcases the opportunities of exponentially accelerating technologies to positively disrupt industry and society. Leading technologists, entrepreneurs, innovators, and industry leaders on such events come together to share case studies, research and technological innovations to integrate cutting-edge technology and science into human lives. For media enquiries, interviews and images, please email: Sophie Curtis [email protected] or call +44 203 287 0590