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MedTech
8 minutes read

AI Drug Discovery: Artificial Intelligence in MedTech

by Robert Kazmi
MedTech
8 minutes read

Artificial Intelligence (AI) promises to forever change the way that we live our lives, and AI drug discovery is one of the more practical and immediate ways Artificial Intelligence can be used to make a huge impact on the human condition. 

When most people think of Artificial Intelligence, they think of sentient robots popularized in science fiction. Modern science has yet to crack the code of true sentient Artificial Intelligence, but it has gotten very effective at using a combination of Machine Learning, Deep Learning, and neural networks to make challenging tasks like drug discovery more efficient. 

Today, it costs pharmaceutical companies nearly three billion dollars to successfully develop one drug and bring it through the entire drug development and clinical trial process. Artificial Intelligence (AI) has the potential to revolutionize the effectiveness of drug discovery and development. 

Let’s explore the ways Artificial Intelligence can improve the drug discovery process, how pharmaceutical and MedTech companies are putting AI to use, and the challenges associated with AI in drug discovery. 

How AI Can Improve the Drug Discovery Process

Finding and developing new, successful drug therapies has gotten more difficult over the years. The main reason is that the bulk of easy-to-find and develop drugs have already been developed. The pharmaceutical industry is now tasked with finding treatments for complex health problems with difficult solutions. 

Currently, the average drug discovery and development process takes around 12 years for a drug to go from the lab, through the trial and approval process, and onto shelves. During that lengthy time, roughly 90 percent of all drug candidates fail during some phase of clinical trials. 

AI in Biotech isn’t exactly new, but more and more pharmaceutical companies are beginning to see the value in using AI technologies in drug discovery. Here are the main ways pharmaceutical companies expect Artificial Intelligence to help in drug development: 

  • Find more drug candidates 
  • Raise the success rate of drug development (drugs that ultimately get approved by regulators)
  • Speed up the entire process of discovery and development

Find More Drug Candidates 

We’ve already touched on the fact that 90 percent of all drugs fail during clinical trials and that finding new drug therapies is getting more difficult since many drugs have already been developed or have previously failed. Artificial Intelligence can help leaders in the pharmaceutical industry identify more drug candidates

In an industry where 90 percent of drug candidates are ultimately failing, identifying more drug candidates can have a huge impact on the number of drugs that make it through clinical trials and into patient’s lives. This increases the odds of finding treatments for difficult to treat conditions that have thus far eluded researchers in the pharmaceutical industry. 

Raise the Success Rate of Drug Development 

A 90 percent rate of failure is not typically very sustainable for companies or entire industries. The high failure rate of drug candidates costs the pharmaceutical industry and ultimately healthcare providers and patients billions of dollars a year. 

There are two main ways that Artificial Intelligence can help researchers increase the rate of success for drug candidates. First, AI and Machine Learning can help identify drug candidates that will ultimately fail in clinical trials earlier in the drug development process before millions of dollars are invested. 

Artificial Intelligence and Machine Learning can also be used to make connections between molecular and cellular reactions that have previously eluded scientists since AI can process and analyze large amounts of raw data. 

These AI tools do not function based on a pre-programmed analytic technique. Machine Learning and AI tools are able to draw their own unique conclusions based on the input data without human bias. This gives them the potential to find new solutions to previously unsolvable problems. 

Speed Up the Entire Process of Discovery and Development

Besides the high rate of failure, the amount of time it takes to identify, design, test, and release a new drug costs companies a lot of money. Even reducing the average drug development time from 12 years to 10 years would be a huge benefit to pharmaceutical companies, the MedTech sector, healthcare providers, and patients. 

Artificial Intelligence can help speed up the entire discovery and development process because it can analyze large amounts of data and form valuable insights and conclusions much faster than a human being. 

Artificial Intelligence and Machine Learning tools only get faster and more accurate as they get more data. So over time, the drug discovery and development process will only continue to get faster if these tools are consistently used. 

How Is Artificial Intelligence Currently Being Used in MedTech?

Almost every major pharmaceutical company has partnered with a MedTech company or created its own internal AI program to take advantage of the power of Artificial Intelligence. There are far too many examples of Machine Learning, Deep Learning, and AI being used in MedTech and drug design and development, to name them all, but we’ll highlight some of the major projects. 

Pfizer has been working with IBM’s Watson for nearly half a decade now to power their search for immuno-oncology drugs. Watson uses a very sophisticated Machine Learning process to analyze data and make decisions. 

Sanofi has teamed up with AI startup Exscientia to use their Artificial Intelligence to more effectively hunt for drug therapies aimed at treating metabolic diseases. 

The Artificial Intelligence tools developed by MedTech company GNS Healthcare have been used by Genentech, a subsidiary of the massive multinational company Roche, to search for breakthroughs and new ideas for cancer treatments. 

These are a few examples of how Artificial Intelligence and Machine Learning are being put to use by some of the largest players in the pharmaceutical industry. There is a lot of potential in the MedTech sector to develop mobile apps and other technologies that use AI to address drug development. 

The Challenges Associated with AI in Drug Discovery 

Despite the large number of benefits Artificial Intelligence tools can have in the drug discovery and development process, there are still challenges associated with the adoption of Artificial Intelligence. 

The main challenges facing Artificial Intelligence in the pharmaceutical industry are:

  • Lack of skilled personnel 
  • Apprehension of replacing humans
  • Data quality 

Lack of Skilled Personnel 

Using Artificial Intelligence in drug discovery requires a unique set of skills. Not only do you need someone to be proficient in operating and understanding AI tools, but you also need a person who understands drug design, biology, and molecular sciences. 

This unique combination of skills is hard to find. Pharmaceutical companies need to invest in training their employees with Artificial Intelligence tools. This is the most effective way to ensure that the unique combination of skills is adequately met. 

Apprehension of Replacing Humans

This challenge is not unique to the pharmaceutical industry. All over the world, there is a general apprehension that AI will replace human beings entirely. Currently, this technology is nowhere near the point of being able to replace humans, and many people wonder if true, sentient AI will ever be accomplished. 

It is true that Artificial Intelligence tools can process and analyze data more efficiently than humans and even draw conclusions that human researchers might not reach. However, this will not replace humans. In fact, more people need to be trained on Artificial Intelligence tools so that they can use them to work more efficiently. 

Data Quality 

Machine Learning and Artificial Intelligence tools are only as good as the data they are receiving. If the data is poor, the conclusions drawn by these tools will not be any more accurate or helpful than the method of trial and error. Ensuring these tools are only receiving quality data is difficult when there are massive amounts of information being processed. 

Final Thoughts

Artificial Intelligence is an important tool that can help save lives by finding drugs that humans have yet to find through trial and error. AI can be used in MedTech app development to create smarter tools that the pharmaceutical industry so desperately needs to improve the time it takes and the amount of money it costs to develop a new drug.

If you have an idea for an app or need help navigating the complexities of Artificial Intelligence, reach out to an app development partner. A development partner can offer you invaluable experience and technical expertise throughout your relationship.

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