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Machine Learning in Drug Discovery and Manufacturing

More data almost always yields better results when it comes to the effectiveness of machine learning, and the healthcare sector is sitting on a data goldmine. As per the estimates by McKinsey, big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.

Drug-quality

From the initial screening of drug compounds to predicted success rate based on biological factors and R&D discovery technologies like next-generation sequencing, the use of machine learning in preliminary drug discovery has the potential for various uses.

Precision medicine seems to be the frontier in this space. It involves identifying mechanisms for multifactorial diseases and in turn alternative paths for therapy. Much of this research involves unsupervised learning, which is in large part still confined to identifying patterns in data without predictions.

MIT Clinical Machine Learning Group is one of the key players in this domain, whose precision medicine research is focused on the development of algorithms to better understand disease processes and design for the effective treatment of diseases like Type 2 diabetes. Microsoft’s Project Hanover is using ML technologies in multiple initiatives. This includes collaboration with the Knight Cancer Institute to develop AI technology for cancer precision treatment, with a current focus on developing an approach to personalize drug combinations for Acute Myeloid Leukemia (AML).

Not just the US, but UK is also venturing into ML’s possibilities in healthcare, with the UK’s Royal Society believing that ML in bio-manufacturing for pharmaceuticals is ripe for optimization. Data from experimentation or manufacturing processes have the potential to help pharmaceutical manufacturers reduce the time needed to produce drugs, resulting in lowered costs and improved replication.

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