How AI is accelerating the growth of biotech into a $31.87 trillion global industry
Sponsored content. This article was supplied by a third party and Business Money has been paid to publish it.
Artificial intelligence has the potential to be a revolutionary innovation for supercharging the development of the global biotech industry, transforming it from a trial-and-error-driven industry into a data-oriented rapid-discovery ecosystem.
According to BioSpace insights, the AI in biotechnology market size is expected to exceed $31.87 billion by 2035, owing to rising demand for faster drug discovery and precision medicine.
The use cases surrounding AI in biotech are already expanding at a rapid pace, and the high-profile institutional adoption of the technology by the FDA, which established its own generative AI assistant to support internal scientific reviews, has emboldened more firms to follow in its footsteps.
This has not only triggered significant global investments but has also opened the door for a more borderless outlook for the sharing of data and high-tech applications in the industry.
As a result, AI is not only breaking down technological boundaries but also geographical ones. With this in mind, let’s take a deeper look at how biotech is set to grow exponentially into one of the world’s most exciting sectors:
Modern drug discovery
One of the key areas of enhancement can be found in the time it takes to bring a new drug to market, which historically has been a significantly extensive and costly process. However, AI is compressing timelines by shifting from ‘wet lab’ biology to more predictive ‘dry lab’ assessments.
Critically, generative AI has paved the way for de novo chemistry, which has allowed researchers to create custom protein binders and antibodies with far greater accuracy.
Use cases like DeepMind’s AlphaFold have also created an environment where AI models can instantaneously predict complicated 3D protein structures, which has helped to unlock brand new cellular targets on an unprecedented scale.
These new examples are already helping to demonstrate how traditional time-to-market rates can be compressed from a scale of multiple years to a matter of months, with far greater levels of accuracy than ever before.
Global participation
As a subset of AI, machine learning (ML) is helping to clear age-old operational bottlenecks in biotech at scale, paving the way for a more conducive environment for the development of new technologies that can be deployed worldwide.
One major innovation with global ramifications is smart trial matching, where AI can aggregate global patient data to identify candidates for rare disease trials, dramatically reducing recruitment times while ensuring regulatory compliance.
These developments come at a time when global biotech initiatives are becoming more accessible than ever, with industry forerunner China now allowing full foreign ownership in selected biotechnology and digital health subsectors, opening the door to fully borderless projects.
Another use of AI that’s opening the door to global participation is the use of digital twins and the ability of virtual models to simulate patient reactions and potential side effects across studies before human testing even begins.
Machine learning can also assist in predictive manufacturing, monitoring bioreactors in real-time and anticipating equipment failures before they risk harming the progress of projects. This can help to ensure that critical data is shared throughout distributed teams without disruption.
Tailored treatments
One of the best use cases of AI can be found in its ability to process multimodal datasets, which can analyze big data produced by DNA sequences, clinical histories, and medical imaging.
This is paving the way for tailored therapies, and we’re already seeing leading biotech firms adopt artificial intelligence to further optimize their treatments. For example, Moderna is using AI to analyze its mRNA sequences to develop personalized cancer vaccines that are tailored to the individual genetic profiles of patients.
Meanwhile, Viome is leveraging ML to scan saliva and stool samples to predict chronic disease risks before any physical symptoms appear. This can help to deliver an era of anticipatory healthcare through biotech treatments that safeguard the health of individuals in a proactive way.
These proactive elements can extend beyond healthcare biotech and into the world of environmental biotechnological solutions that can help to care for the planet.
For instance, neural networks can analyze satellite, drone, and sensor data to detect the early signs of crop disease of degredation, which is expected to be a growing threat in a world that’s increasingly impacted by climate change. This application of the technology can help to prevent agricultural losses, supporting a healthier ecosystem as a whole.
The future of biotech
Artificial intelligence, and its ability to deploy machine learning tools to assist in the development of biotech solutions in particular, is set to transform the future of healthcare on a global scale.
The technology will play a leading role in converting healthcare from a reactive to a proactive industry, supported by global collaboration and an unprecedented level of personalization.
With the assistance of AI, we’re already seeing cutting-edge new technologies entering the fray for leading biotech players, and patients around the world will be in line to reap the rewards over the years ahead.