With the evolution of computing in the past decade, numerous fields have benefited significantly. In fact, almost 90 percent of the data in the world was gathered in the past two years, which is beyond human comprehension.
For this reason, coming up with analytic methods that can interpret the massive load of data is vital to understanding the hidden knowledge.
The pharmaceutical industry makes up one of the fields that can profit considerably from advancement in computing.
Currently, the process of recognizing a new molecule and marketing it as an approved drug can take between 15 and 20 years, even with today’s technological advances. Even so, the success rate for developing a new drug remains at 5 percent and costs more than $2 billion for each new medicine.
How can AI Transform the Process of Drug Development?
The average academic researcher studies between 250 and 400 articles each year. Currently, with the enormous library of information about disease pathogenesis, analyzing each bit of information available on every biomarker is impossible for humans.
Since researchers focus only on their field of work, they tend to read only on their area of specialization. This situation eliminates the possibility of connecting their knowledge with other fields.
In fact, there is a high chance that many diseases come from psychological and pathological factors that may have been drawn from different specialties, which are yet to be connected.
For instance, Alzheimer’s disease is regarded as an outcome of the interconnection between neurological and immunological aspects.
With the help of AI, researchers can analyze more information beyond human capability. This benefit can allow them to connect, assimilate and correlate data. Additionally, AI can help researchers to structure valuable information from different sources to help them look deeper into the details.
What AI has in store for the Pharmaceutical Industry
Drug target identification and clinical testing are pain-staking processes. However, thanks to AI, their efficiency can be enhanced.
Benevolent AI has predicted about 60 percent time-saving in early drug development stages in comparison to traditional pharmaceutical sector averages.
The treatment advances for Amyotrophic Lateral Sclerosis (ALS) best accentuate AI’s promise of efficiency in the development phase.
By using an internally-developed AI platform, billions of paragraphs and sentences from millions of abstracts and papers were analyzed, which brought together direct links and relationships within the data. This process produces previously undiscovered treatments from approved scientific and peer-reviewed papers.
Although the AI platform developed by Benevolent AI was initially made to comprehend the hidden cases of diseases and suggest treatments, it can explore other numerous R&D processes.
Some of these processes include forecasting the properties of chemical compounds, minimizing the number of other complementary compounds to be synthesized and boosting the chemical palette available to chemists in the medical field.
Furthermore, the AI’s ability to access and evaluate different datasets can present various opportunities during the designing of clinical trials.
AI Promises to Strengthen not to Replace
AI aims at building upon and leveraging the vast amount of knowledge gathered by researchers as opposed to replacement. It hopes to ultimately reduce the errors and save time throughout the drug discovery process.