A deadly public health crisis, thousands of diseases without treatments, and healthcare spending in the trillions of dollars globally- it’s clear that we need innovation now to accelerate drug discovery. The COVID-19 pandemic has put a laser focus on drug discovery, and how technology can help improve the 90% industry failure rate and lengthy time to market. The deluge of digital data in just the past few years gives us a prime opportunity to improve the drug development process with AI.
Today, we can produce more biomedical data in about three months than the entire 300-year history of healthcare. No human can synthesize that level of data, and this is where artificial intelligence can make a crucial impact. AI is the most powerful technology force of our time. It is software that writes software that no human can.
Researchers worldwide are racing to find an effective vaccine and drug candidates to inhibit infection with and replication of SARS-CoV-2, the virus that causes COVID-19. Graphics Processing Units (GPUs) are accelerating this lengthy discovery process — from predicting protein structures to molecular docking, to generative, to virtual screening, and high-throughput screening.
To develop an effective drug, researchers must know where to start. A disease pathway — a chain of signals between molecules that trigger different cell functions — may involve thousands of interacting proteins. Understanding the mechanism of disease and the molecular pathways involved is crucial. Genomics can help in helping researchers find which genes are affected in diseases, helping them identify promising proteins to target genome analysis.
Given the unprecedented spread of the COVID pandemic, getting test results in hours versus days has had an extraordinary impact on identifying and stopping the spread of the virus. Hundreds of institutions, including hospitals, universities, and supercomputing centers across the world are using various genomic tools to accelerate their work, from sequencing the viral genome, to rapid testing, to analyzing the DNA of COVID patients to investigate why some patients are more severely affected by the virus versus others.
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 institutions are investing in AI to both accelerate their workflows, bring disparate patient data together, accurately analyze genomes and visualize protein confirmations, monitor patients and help with patient diagnoses. During the COVID-19 pandemic, momentum around AI for healthcare has accelerated, with startups estimated to have raised well over $5 billion in 2020.
The tremendous focus of AI on a single problem in 2020, like COVID-19, really showed that with that focus, we can see the benefits from artificial intelligence and help reinvent the future of drug discovery.
