Artificial intelligence and machine learning could be an invention comparable to electricity, so what does the future hold for its use in drug discovery?

There are many reasons why it is crucial to understand how an AI-enabled system has led to a specific output. Explainability can help developers ensure that the system is working as expected, meet regulatory requirements, or reveal the importance and effects of data previously deemed unimportant by humans.

Some of the biggest current focuses of machine learning and AI research and development include testing clinical scenarios to identify the patients who will benefit most from treatment. One of the main goals for researchers is increasing the transparency and understanding underpinning data sharing and AI technology.

The pharmaceutical industry is working on improving the usability of automation platforms but challenges remain.

One of the most impactful advances in stem cell research of the past decade has been the development of organoid culture techniques.

Progress in protein degradation has accelerated in the past few years, and 2021 saw several breakthroughs. Efforts to target STAT3, however, have proven to be especially challenging.

RAS mutations are the most common subfamily of oncogenes, so finding inhibitors could be extremely valuable to oncology research. However, they also make for particularly difficult targets. Andreas Mantoulidis explains how BI have been attempting to ‘drug the undruggable.’

Digital tools to boost productivity and optimise pharma manufacturing are becoming increasingly prevalent through smart sensors, AI, ML, virtual reality, and cloud computing. Digital transformation is here for Pharma, and it is changing companies' priorities towards digital solutions.  

How does the MSD group find its targets for neuroscience drugs?

Presented by Alex Titterton, Field Applications Engineer, Graphcore