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Machine Learning in Pharmaceuticals

The Frick Chemistry Laboratory, Princeton University
(The Frick Chemistry Laboratory, Princeton University - Kimberly Chen)

 

Machine Learning (ML), one of the most prominent approaches in artificial intelligence, is the future of pharma. The human genome project and thousands of subsequent discoveries at the DNA, RNA, and protein levels were made possible by ML's ability to detect patterns across large and often messy data sets. ML has the potential to expedite the clinical drug discovery and development process by applying sophisticated algorithms to the analysis and mining of different data sources to predict molecule behavior and suitability as drug targets or therapeutic entities. 

The current drug discovery process – too lengthy and very expensive. It can take up to 15 years to translate a drug discovery idea from initial inception to a market ready product. Industry is currently said to spend well over $1 billion per drug. That’s partly because all the drugs that didn’t make it have to be paid for. As our understanding of biology deepens thanks to the availability of new data and algorithms capable of learning from it, the drug discovery process is literally being transformed. ML presents the pharmaceutical industry with a real opportunity to do R&D differently, so that it can operate more efficiently and substantially improve success at the early stages of drug development. 

The drug discovery process and the researchers that drive the pipelines can be greatly aided by the latest innovations in ML technology. The average biomedical researcher is dealing with a huge amount of new information every day. It’s estimated that the bioscience industry is getting 10,000 new publications uploaded on a daily basis – from across the globe and among a huge variety of biomedical databases and journals. So it’s impossible for researchers to know, let alone process, all of the scientific knowledge out there relating to their area of investigation. What’s more, without the ability to correlate, assimilate and connect all this data, it’s impossible for new usable knowledge – which can be used to develop new drug hypotheses – to be created. 

ML has a vital role to play in augmenting the work of drug development researchers so that an informed, first analysis of the mass of scientific data can be conducted in order to form essential new knowledge. What was once an entirely hypothesis driven approach where humans posed the questions is shifting toward scientists starting with an outcome and using machine learning to help discover important relationships to that outcome within the data. 

ML will also help in terms of the industry’s selection of patients for clinical trials and enable companies to identify any issues with compounds much earlier when it comes to efficacy and safety. So the industry has much to gain by adopting ML approaches. It can be used to good effect to build a strong, sustainable pipeline of new medicines.

 

 

[More to come ...]


 

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