AI, Robotics Team in Drug Discovery
By Martin-Immanuel Bittner  |  Oct 26, 2021
AI, Robotics Team in Drug Discovery
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The COVID-19 pandemic has highlighted more than ever the need for new treatments and drug discovery, which is generally a slow, expensive process that often ends in failure. Martin-Immanuel Bittner argues that previous problems will be overcome by combining robotics and data science, and richer, more reliable data can be generated at speed, enabling better decisions in both human-driven and computational drug discovery.

BOSTON - With COVID-19 still dominating headlines around the world, a greater shift in the public appreciation of biomedical research and drug discovery is underway. Increased media coverage of the time and cost involved in developing new drugs and vaccines have been in the spotlight, including questions about the efficacy and speed of the process, as well as the impact of its outcomes. 

As a medical doctor who moved into research and completed a DPhil in Oncology at the University of Oxford, my motivation is to bring new and better therapies to patients around the world. New treatments are desperately needed, not just for COVID-19, but for cardiovascular diseases, cancer, neurodegeneration, and many other ills. I was a resident oncologist before moving into drug discovery, and so know first-hand the need for better treatments. 

Traditional drug discovery is slow, expensive, and error-prone. Most research is done manually. Options for lab-based research are either in-house scientists or outsourced experimentation using contract research organizations (CROs). Both are fraught with disadvantages: inefficient deployment of intellectually valuable human resources; low utilization rates of expensive scientific equipment; operational inflexibilities; low

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