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How Technology Could Transform Drug Research in 2022

How Technology Could Transform Drug Research in 2022

When we think of new technologies in medicine, we tend to conjure images of futuristic AI computers, 3D-printed organs, and robot surgeons. The ambitious and lesser-explored methods currently being applied in drug discovery and development, however, could prove to be just as exciting.

A GlobalData survey this year revealed that over 70% of pharma industry respondents anticipate drug development will be the area most impacted by the implementation of smart technologies. As the year draws to a close, Pharmaceutical Technology takes a look at some of the technological innovations and approaches that could transform drug research in 2022.

Harnessing AI with supercomputing

Supercomputers are vastly superior to general-purpose computers in terms of speed and performance and are particularly valuable when it comes to performing scientific and data-intensive tasks. It makes sense, then, that researchers are looking to apply supercomputing to the exhaustive process of drug discovery and design.

This year, US tech company NVIDIA launched Cambridge-1, the UK’s most powerful supercomputer, to help British healthcare researchers solve some of the industry’s most urgent healthcare challenges. Along with the launch of Cambridge-1, NVIDIA also announced a series of collaborations with the pharma behemoths AstraZeneca and GlaxoSmithKline, and institutions like Guy’s and St Thomas’ NHS Foundation Trust, King’s College London, and Oxford Nanopore Technologies.

The Cambridge-1 supercomputer has the potential to significantly accelerate and optimize every stage of drug research. NVIDIA is collaborating with AstraZeneca to build a transformer-based generative AI model for chemical structures, which will allow researchers to leverage massive datasets using self-supervised training methods and enable faster drug discovery.

GSK’s research has a steadfast focus on genetically validated targets, which are twice as likely to become approved therapies and now make up more than 70% of the company’s drugs pipeline. NVIDIA has partnered up with GSK and its AI team to unlock vast quantities of genetic and clinical data and help the company to develop more effective drugs and vaccines, faster.

NVIDIA’s vice president of healthcare Kimberly Powell shared with Pharmaceutical Technology the company’s top three predictions for supercomputing in pharma:

AI accelerates million-times drug discovery: “ Molecular simulations help to model target and drug interactions completely in silico. The breakthroughs of AlphaFold and RoseTTAFold that created a thousand-fold explosion of known protein structures, and AI that can generate a thousand more potential chemical compounds has increased the opportunity to discover drugs by a million times”.

Multimodal AI: “There are over ten thousand diseases without a therapy. Multiple sources of health data need to be used, whether it is to discover drugs or treat patients. To leverage the world’s largest data sources, multimodal AI will bring us to that new frontier in discovering disease pathways, as well as personalizing the treatment and prognosis of patients”.

AI 2.0 with federated learning: “To help application developers industrialize their AI technology and expand the application’s business benefit, AI must be trained and validated on data that resides outside the possession of their group, institution, and geography. Federated learning is key to enabling such collaboration to build and validate robust AI models without sharing sensitive data. Federated learning will be an essential capability to facilitate the continuous learning and evaluation of AI”.

While Cambridge-1 may be the most powerful supercomputer in the UK, Japan is home to the world’s fastest. Fugaku, jointly developed by research institute RIKEN and tech company Fujitsu, aims to tackle a range of pressing scientific and social issues. For healthcare, this means drug discovery through functional control of biomolecular systems, and integrated computational life science to aid the development of personalized and preventive medicine.