AI has been incredibly prevalent within the Biotech space, especially within Drug Discovery and Research and development. AI tools are enabling Biotech and other areas of Life Sciences to drastically propel their drug lifecycle forwards, enabling drugs to go to market substantially faster than they would have been able to previously.
This is a considerable win for the Life Sciences industry as well as for the patients who desperately need new drugs and treatments. The positive buzz surrounding AI within Biotech is encouraging investors to pump more money into organisations that are adopting AI – which shows that this should have a continuous positive impact in years to come.
OMDENA highlighted 5 key ways in which AI is benefitting the Biotech space:
We’ve picked two key Biotech’s which highlight how Machine Learning is advancing and revolutionising how they operate.
Turbine is a Biotechnology that has developed a platform powered by Machine Learning, which is an application of AI, to delve deeper into our understanding of cancer and ultimately provide better data and knowledge to create patient-centric solutions to tackle this disease.
“Pioneering an approach that combines simulation with machine learning, we map and model how thousands of signaling proteins interact characterizing cellular level cancer behavior and response or resistance to treatment. Our platform enables the simulation of drug-like effects from compounds that may not exist yet, on cells potentially unavailable for lab-based testing, like those of high unmet need cancer patients.”
Turbine has harnessed technology, AI application and an incredible drug development team to create a complex but efficient methodology so we can understand how cancer cells operate, thus enabling us to target and mitigate that cancer quickly.
Machine Learning has been widely used by many other Biotechnologies in therapeutic areas outside of Oncology, and is one of the most widely-used applications of AI due to its successes but also how versatile it can be. From gene sequencing to understanding neural networks, Machine Learning goes beyond what we can achieve as humans but with a much quicker time scale. Although Machine Learning isn’t considered “new” or “innovative” due to its tenure within Biotech, it still continues to shake up the space, thus creating demand for talent within this area.
Accutar Biotech uses AI to accelerate its drug development process for its drug pipeline within Oncology. They also have created a complete AI solution workflow for preclinical drug discovery.
Unlike Turbine which focuses on using Machine Learning specifically to map and model cellular-level cancer behaviour, Accutar instead has developed their version of digital platforms powered by Machine Learning to amplify what they call their “traditional approaches” for preclinical drug discovery.
A great example is their ChemiRise system, which works by using previous data from their databases to accurately and quickly create an atom-mapping algorithm “showing satisfying functionality and a potential productivity boost in real-life use cases”. It’s important to know that although Accutar’s ChemiRise system is entirely built to run as an AI platform, it underwent thorough checks to ensure its accuracy before going public. Accutar is now distributing this system which can enable other organisations to benefit, thus creating a functional and reliable methodology that can be used in the drug development process.
“Like computers, humans are nothing more than a bunch of data strung together.
At their core, computers are just a bunch of 1s and 0s coded in sequence, with each number corresponding to a certain action for the computer to perform. Human DNA is much the same. We’re just a bunch of As, Gs, Cs, and Ts strung together. Those are the four base types found in human DNA molecules, each determining a person’s characteristics, traits, and even actions.” – Investor Place
In summary, the advances and growth that we are seeing, specifically within therapeutic areas such as Oncology – show that AI – specifically Machine Learning, is here to stay. We’re excited to see how Biotechnologies are going to continue to benefit from AI, particularly in the Drug Discovery process, and how this is going to affect drugs going to market over the next five, ten, and fifteen years.