If you’ve watched TV more than once in the past decade, you’ve probably heard something about artificial intelligence (AI).
The prospect of human-like intelligence in machines has captivated our collective imagination since the first computers made their first calculations. Though the concept of AI is not new, in recent years AI technology has advanced rapidly. Computers are now able to engage with complex datasets, learn from them, and use their findings to make startling insights and predictions.
Many companies are already using AI technology to analyze data and interact with customers. In an increasingly data-driven world, AI is poised to influence almost every aspect of our lives in the coming years.
The pharmaceutical industry is no exception.
From major drug giants to biotech startups, hundreds of companies are already leveraging AI to expand and improve their offerings. These are a few ways that drug discovery research is already changing with the times.
Review the Literature
Every year thousands of peer-reviewed articles are published and billions of health data points are generated. Even focusing on a narrow field, it would be almost impossible for a single researcher to keep track of every experiment and minor finding associated with their specialty, and even more difficult to weave them into new insights.
Many companies are now using AI to keep track of the flood of research health data available.
Far beyond just aggregating related studies, AI is able to read through thousands of results to illuminate subtle trends and build meaning from a range of loosely related findings.
A task that would take a human years to complete can be done in the span of hours. Researchers can use the reports generated by AI to detail the mechanisms of a disease, identify interesting new drug candidates, and review existing research.
Deconstruct Disease
Understanding how a disease works is one of the best starts to finding a cure.
Companies are using AI to analyze disease research, patient data, and structural models to find the underlying mechanisms of diseases and identify potential biological targets for a drug to influence. These analysis allow researchers to go beyond treating symptoms and find the underlying basis of what causes a disease and how it progresses.
Even well-researched diseases may have molecular underpinnings that doctors hadn’t previously recognized as a potential weak spot for the pathogen. AI can identify these weaknesses and help scientists understand how to exploit them for the benefit of the patient.
Identify New Molecules
Once a biological target for a disease is identified, the next step in drug discovery is identifying molecules that will interact with these targets to treat the disease.
AI can be used to model molecules that will be perfect fits for a target or that may interact with a disease in unexpected ways.
In reverse, scientists can use AI to analyze poorly understood natural compounds and predict how they will behave in the body. In both of these situations, AI can also be utilized to predict if a compound will go beyond interacting with a disease to cause other unintended consequences in the body. This can help rule out drug candidates that would be too toxic for safe use.
Engage in Preclinical Testing
Analyzing compounds prior to clinical trials is a critical part of drug discovery that can also be time consuming and expensive. AI can be used in multiple ways throughout the preclinical process to streamline and cut costs.
Researchers can use AI to plan experiments, suggest which cell lines to test in, automate laboratory processes, and perform data analysis.
AI can help explore results from specialized cell lines or animal models to suggest how a drug is likely to impact a human patient based on comparisons to previous studies. AI helps to eliminate unnecessary experiments and make the most of all collected data.
Optimize Clinical Trials
A critical final step in drug discovery is ensuring that a candidate drug is safe and effective in humans.
AI can help pharmaceutical companies be confident going into clinical trials and can also help them run clinical trials more efficiently.
From planning successful trials, to recruiting ideal patients, to collecting and interpreting data, AI helps researchers run clinical trials smoothly and make the most of patient participation.
AI can also help identify subtle symptoms that might be more serious in some patients and could mean trouble for a drug if it goes to market for a larger population. Clinical trials are expensive. AI helps reduce risk and maximize reward, for both patients and researchers.
The Future of Drug Discovery
Over the next decade, AI will continue to transform the way pharmaceutical researchers find and develop new medications. By reducing false starts and enhancing research efforts, AI will help accelerate drug discovery for a healthier tomorrow.
The only question is, how far will AI take us?
Let us know in the comments.