Home Healthcare Why Biotech and Big Pharma are Counting on AI

Why Biotech and Big Pharma are Counting on AI

The development of new drugs is a painstaking process that requires both time and money. In fact, the process entails a decade of research and a whopping budget of $2.6 billion to take a drug from the laboratory to the market. Also, with effectiveness and safety concerns, only 5 percent of such medicines reach the market.

In response to drug discovery challenges, tech companies and drug makers are investing billions in artificial intelligence with the hope of making the process cheaper and faster. Eric Hortiz, Microsoft Research Labs’ director, said that artificial intelligence is a sleeping giant for the healthcare industry. He also added that Microsoft was focusing on pharmacology and drug design.

Findings from BenchSci, a Toronto-based biotech entity, indicated that Microsoft was not alone in AI investing, as more than 60 startups and 16 pharmaceutical companies are utilizing AI for drug development or discovery today.

MORE – Top 10 Ways Artificial Intelligence is Impacting Healthcare

MORE – Big Data: All the Stats, Facts, and Data You’ll Ever Need to Know

The Limitations of Drug Development

Most of the limitations in drug discovery usually exist in the early phases of research. In this case, the issues involve the time required to move from recognizing a potential disease target to conducting tests in a bid to identify whether a drug candidate can hit the particular target.

Currently, ambitious AI groups like ATOM are targeting to compress the duration, which takes four-six years on average, to one year. Although this goal is yet to be accomplished, BenevolentAI, dubbed as Europe’s biggest private AI firm, asserted that it has dramatically minimized the number of trial and error required to create a drug candidate after identifying a potential disease target. What ’s more, the company approximates that it can minimize the drug design duration from three years to one and reduce the related costs by 60 percent.

Centered on Failure

The head of GlaxoSmithKline’s Baltimore-based AI drug development unit, John Baldoni, argued that the pharmaceutical industry is based on failure considering that the odds of developing a new drug successfully are poor. However, he added that the same testing that is used to rule out some drugs usually contains vital data, which could be used in other processes.

Achievements by Benevolent AI

BenevolentAI prides itself on the expansion of its collection to include over 20 drug programs that target not only rare cancers but also neurodegenerative, central nervous system and inflammatory diseases. Furthermore, the company has utilized other AI algorithms to find new treatments for amyotrophic lateral sclerosis (ALS). Even so, BenevolentAI recognized five drug candidates for ALS in a week’s time in 2016. The entire process would have taken years to complete without AI.

Problems with Watson

Many people are still not convinced that AI can revolutionize the drug development process. To top it up, IBM Watson has elicited negative publicity in recent months. Experts doubt Watson’s capability as far as interpreting sophisticated medical data is concerned.

According to Dr Alan Aspuru-Guzik, a chemist at Harvard University, the main problem with using AI for drug development includes gathering credible information to train Watson and other AIs. He emphasized his point by saying that for an AI system to be excellent, the data has to be excellent too.

Source NBCNews

Subscribe to our newsletter

Signup today for free and be the first to get notified on the latest news and insights on artificial intelligence

KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
- Advertisment -


AI Model Development isn’t the End; it’s the Beginning

AI model development isn’t the end; it’s the beginning. Like children, successful models need continuous nurturing and monitoring throughout their lifecycle. Parenting is exhilarating and, if...