The COVID-19 pandemic has put a spotlight on drug discovery, which encompasses microscopic viewing of molecules and proteins, sorting through millions of chemical structures, in-silico methods for screening, protein-ligand interactions, genomic analysis, and assimilating data from structured and unstructured sources.
The typical drug discovery process takes about a decade, costs $2bn and suffers a 90% failure rate during clinical development. But the rise of digital data in healthcare in recent years presents an opportunity to improve those statistics with AI.
How AI can reduce drug discovery timelines
Today, we can produce more biomedical data in about three months than the entire 300-year history of healthcare. This is now becoming a problem as no human can really synthesise that level of data, and thus the industry needs to call upon artificial intelligence (AI).
AI is the most powerful technology force of our time. It is software that writes software that no humans can. Researchers worldwide are racing to find effective vaccine and drug candidates to inhibit infection with and replication of SARS-CoV-2, the virus that causes COVID-19.
Graphic Card Units (GPUs) are accelerating this lengthy discovery process – whether for structure-based drug design, molecular docking or generative AI models, virtual screening or high-throughput screening.
To develop an effective drug, researchers have to know where to start. A disease pathway – a chain of signals between molecules that trigger different cell functions – may involve thousands of interacting proteins.
Genomic analyses can provide invaluable insights for researchers, helping them identify promising proteins to target with a specific drug.
With genome analysis toolkits, researchers can sequence and analyse genomes up to 50x faster. Given the unprecedented spread of the COVID-19 pandemic, getting results in hours versus days can have an extraordinary impact on understanding the virus and developing treatments.
Hundreds of institutions, including hospitals, universities and supercomputing centres across the world, are using this kind of software to accelerate their work – to sequence the viral genome itself, as well as to sequence the DNA of COVID-19 patients and investigate why some are more severely affected by the virus than others.
But AI works best when it is domain-specific, combining data and algorithms tailored to a specific field like radiology, pathology or patient monitoring.
Application frameworks bridge this gap by providing researchers and clinicians the tools for GPU-accelerated AI in medical imaging, genomics, drug discovery and smart hospitals.
Healthcare ecosystem rallies around AI
Accelerated computing spending within healthcare is growing at a rapid pace, driven by the increasing computational demand for AI in areas of drug discovery, genomics and imaging.
Amid the COVID-19 pandemic, momentum around AI for healthcare has accelerated, with start-ups estimated to have raised well over $5bn in 2020.
We are seeing more healthcare start-ups than ever harness the power and support of established accelerator programmes too, with record numbers of AI healthcare papers being submitted showing the exponential growth over the past decade.
Leading research institutions like the University of California in San Francisco are also using GPUs to power their work in cryo-electron microscopy, a technique used to study the structure of molecules – such as the spike proteins on the COVID-19 virus – and accelerate drug and vaccine discovery.
And pharmaceutical companies, including GlaxoSmithKline, and major healthcare systems, like the UK’s National Health Service (NHS), will harness technologies like the Cambridge-1 supercomputer – the UK’s fastest AI supercomputer – to solve large-scale problems and improve patient care, diagnosis and delivery of critical medicines and vaccines.
The clinical community is using federated learning approaches to build robust AI models across various institutions, geographies, patient demographics and medical scanners. The sensitivity and selectivity of these models are outperforming AI models built at a single institution, even when there is copious data to train with.
As an added bonus, researchers can collaborate on AI model creation without sharing confidential patient information. Federated learning is also beneficial for building AI models for areas where data is scarce, such as for paediatrics and rare diseases.
In the UK, King’s College London and Owkin are creating a federated learning platform for the NHS. The Owkin Connect platform enables algorithms to travel from one hospital to another, training on local data sets.
It provides each hospital with a blockchain-distributed ledger that captures and traces all data used for model training.
The project is initially connecting four of London’s premier teaching hospitals, offering AI services to accelerate work in areas such as cancer, heart failure and neurodegenerative disease, and will expand to at least 12 UK hospitals in 2021.
Software-defined instruments link AI innovation and medical practice
Software-defined instruments, devices that can be regularly updated to reflect the latest scientific understanding and AI algorithms, are key to connecting the latest research breakthroughs with the practice of medicine.
AI, like the practice of medicine, is constantly learning. We want to learn from the data and we want to learn from the changing environment.
By making medical instruments software- defined, tools like smart cameras for patient monitoring or AI-guided ultrasound systems can not only be developed in the first place, but also retain their value and improve over time.
UK-based sequencing company Oxford Nanopore Technologies is a leader in software- defined instruments, deploying a new generation of DNA sequencing technology across an electronics- based platform.
Its nanopore sequencing devices have been used in more than 50 countries to sequence and track new variants of the virus that causes COVID-19, as well as for large-scale genomic analyses to study the biology of cancer.
The company uses GPUs to power several of its instruments, from the handheld Point of Care MinION Mk1C device to its ultra-high throughput PromethION, which can produce more than three human genomes’ worth of sequence data in a single run.
To power the next generation of PromethION, Oxford Nanopore is also adopting new technologies to enable its real-time sequencing technology to pair with rapid and highly accurate genomic analyses.
For years, the company has been using AI to improve the accuracy of base calling, the process of determining the order of a molecule’s DNA bases from tiny electrical signals that pass through a nanoscale hole, or nanopore.
This technology truly touches on the entire practice of medicine, whether COVID epidemiology or in human genetics and long-read sequencing.
Through deep learning, the base calling model is able to reach an overall accuracy of 98.3%, and AI-driven single nucleotide variant calling gets them to 99.9% accuracy.
The path forward for AI-powered healthcare
We are also seeing the emergence of platforms that use intelligent video analytics and automatic speech recognition technologies to help a new generation of smart hospitals perform vital sign monitoring while limiting staff exposure.
These application frameworks facilitate a critically needed ecosystem of AI solutions for hospital public safety and patient monitoring by transforming everyday sensors into smart sensors.
Critical use cases included automated body temperature screening, protective masks detection, safe social distancing and remote patient monitoring.
Partners across the ecosystem are using pre- trained models and transfer learning to develop and deploy AI applications that combine speech, vision and natural language processing.
AI-powered breakthroughs like these have grown in significance amid the pandemic. The tremendous focus of AI on a single problem in 2020, like COVID-19, really showed that with that focus, we can see every piece and part that can benefit from artificial intelligence.
What we have discovered over the last 12 months is only going to propel us further in the future. Everything we’ve learned is applicable for every future drug discovery programme there is.
Source: PMLive