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Machine Learning Drug Discovery Platform

One Million Solutions in Health™ and Quantitative Medicine Announce a New Opportunity to Accelerate Drug Discovery Through A Machine Learning Drug Discovery Platform

Together with Quantitative Medicine, we are pleased to announce a new opportunity for the Life Sciences industry. This unique opportunity will enable industry participants to evaluate an innovative, machine learning drug discovery platform which was conceived after decades of research at the Center for Computational Biology at Carnegie Mellon University.

The Computational Research Engine™ (CoRE™) creates and leverages highly accurate, predictive models to accelerate the discovery of drugs, therapeutics and diagnostics. This machine learning drug discovery platform substantially improves pharmaceutical and biotechnology research and development productivity by efficiently directing experimentation. Fundamentally, CoRE™ recommends ‘what to do next’, thereby increasing the utility value of each experiment. This unique capability enables researchers to gather only the most essential information, at the lowest cost, and within the shortest timeframe.

For scientists in academia, industry, consortia, government or those conducting research and development on behalf of patient foundations – this opportunity is unique. Quantitative Medicine will be offering a limited number of organizations a valuable opportunity to assess – using their own data – CoRE’s ability to speed up the drug discovery process, through their partnership with us.

The opportunity to accelerate drug discovery is now available to any organization recognizing the benefits of efficiently and concurrently directing experimentation in preclinical drug discovery.

While predictive modeling is not new, when a large number of potential compounds need to be tested, such as in drug discovery, active machine learning can play a crucial and central role in prioritizing the experimental work flow.

Quantitative Medicine’s goal is to use sophisticated computation to effectively guide experimentation. By using this approach, experimentation is directed to cost-effectively explore very large experimental spaces, predicting the effects of millions of putative drug compounds on thousands of diverse targets. In twenty-one proof-of-value studies performed for large pharmaceutical companies, Quantitative Medicine has demonstrated CoRE™ can capture cost reductions of 50% to 90%, while reducing time to accomplish those research objectives by a minimum of 50%.

Industry organizations can now participate in evaluating how CoRE™ can pick up the pace of discovery, allowing scientists to accomplish their research goals in the least amount of time and at the lowest cost.

As a result of our oversight and management of multiple pre-competitive collaborations regarding the CoRE™ machine learning drug discovery platform, Quantitative Medicine is entering into contract negotiations with several major pharmaceutical companies to help direct their drug discovery and development campaigns.

CoRE™ is therapeutic agnostic and a natural fit wherever a company has limited resources and is trying to prioritize the best path forward. Some obvious applications are early screening and lead series optimization.