How AI is Transforming Drug Discovery

By Miranda Sweeney | June 14, 2018



Bringing a new pharmaceutical drug to market takes about 12 years and can reach into the billions in R&D expenditures, industry leaders are now seeking more efficient methods of approaching this process and AI is emerging as a potential solution.  



The interest in AI-driven solutions for early stage drug discovery is growing steadily among biopharma leaders with a projected market volume reaching $10B by 2024. With a growing number of groundbreaking AI use cases in other hi-tech industries -- ranging from self-driving cars to speech and image recognition tools to personal assistants i.e. Siri and Alexa-- players in the biopharmaceutical industry are looking toward AI to speed up drug discovery, cut R&D costs, decrease failure rates in drug trials, and eventually create better medicines.

In the past, drug companies have used artificial intelligence to examine chemistry—whether a drug might bind to a particular protein, for instance. But now the trend is to use AI to probe biological systems to get clues about how a drug might affect a patient’s cells or tissues. Biological insights driven by machine learning and AI also could help pharmaceutical companies better identify and recruit patients for clinical trials of therapies most likely to work for them, perhaps boosting the chances of those medications’ getting approved by regulatory agencies such as the Food and Drug Administration.

Studies like Project Survival which aims to discover and validate the first ever clinical biomarker to diagnose and treat pancreatic cancer, are using AI to make drug research and development less expensive and more efficient. Intelligent machines scour patient samples and genes, along with those of hundreds of other patients, for molecular fingerprints, or biomarkers, that could later be used to help measure a specific drug’s impact and to identify patients in which such a drug is likely to be most useful. The rise of precision medicine is putting pressure on drug developers, steering them away from a one size fits all model. Who knows what interesting developments we could see next.

Topics: Robotics, Automation, Drug Discovery, Pharma, R&D, Big Pharma, AI, Artificial Intelligence, Machine Learning