Lantern will use their artificial intelligence (AI) platform, RADR, to accelerate the development of ADC candidates from discovery to clinical trials within a timeframe of approximately two years.

Govinda Bhisetti, Principal Investigator and Head of Computational Chemistry at Biogen, leads a discussion group on what the revolutionary machine learning technology means for drug design.

How are AI integration and the growing capabilities of machine learning impacting drug discovery and clinical trials?

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.

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.  

Presented by Alex Titterton, Field Applications Engineer, Graphcore

There are innumerable possible compounds in Merck KGaA’s virtual library, so finding drug candidates is only possible with the help of computational methods.