Medical researchers are engaged in a never-ending struggle to discover better treatments for the endless number of disorders, pandemics, and diseases that plague humanity. Whether it’s against a long-time foe like diabetes, cancer, or Alzheimer’s, or a sudden appearance of the latest viral epidemic, biochemists need every tool they can get to find successful treatments.
Fortunately, traditional drug discovery has some new allies in the fight — artificial intelligence and machine learning. We will explore how AI and ML are boosting today's scientific research and how these tools can also work with other industries.
Artificial Intelligence and Machine Learning: A Quick Refresher
Let's take a quick moment to review what AI and ML are and their differences.
Artificial Intelligence is a rapidly growing technology that enables machines to perform functions and tasks that simulate human thinking and behavior. AI uses algorithms and programming. Machine learning is an AI technique that enables a machine to learn automatically from previous data without being specifically programmed. ML uses structured and semi-structured data.
The Old Way of Performing Drug Research
Before the advent of AI and ML, scientists had no choice but to take a traditional, manual approach to drug discovery. The process relied heavily on trial and error, hypothesizing how certain chemical compounds would react to diseases and other disorders. Researchers created or identified a wide range of potential candidate molecules, usually thousands, and studied each candidate’s effect to see which may result in successful treatments.
Scientists would narrow down the candidates by performing in-vitro testing in labs and finding the safest ones, then use them in human trials. This research method is a slow, painstaking, expensive process. On average, only one molecule out of a thousand tested makes it to clinical trials. And if a drug makes it to the clinical trial stage, only one in ten make it to approval.
The research and development process of finding 10,000 candidate molecules to reach one viable treatment takes years, as much as five years to get to clinical trials, which in turn could take another five years. The entire time-consuming process costs an average of $2 billion.
The COVID pandemic underscored the weakness of this traditional research method. A highly contagious, fast-moving virus needs a fast but safe and effective response. That's why the pharmaceutical industry needs the speed and efficiency of digital technologies to create solutions faster and safer.
How AI and ML Improve Pharmaceutical Research
Nowadays, thanks to AI and machine learning, researchers can use these technologies to make considerably more focused hypotheses, identifying smaller sets of eligible candidates from existing compounds. This efficient process takes less time, effort, and investment, and delivers better solutions faster. Researchers can use AI and ML to create new drugs and use them to run tests to discover new uses for existing medications.
Given the potential of AI and ML, it’s likely that all drugs could be designed digitally by 2030. Consider the prospect of intelligent computers sifting through vast databases of chemical molecules, identifying and cataloging their qualities, and flagging those that could be useful in future treatments against diseases. Now picture this process happening at lightning speed, staying one step ahead of future pandemics, and performing these functions with complete accuracy.
For example, Exscientia, a leading pharmatech company that is the first to automate drug discovery, using what they call the Centaur Chemist™ approach. It’s an appropriate term since a centaur is a man-horse hybrid, and the company relies on the efforts of both humans and machines to power their drug discovery efforts.
Exscientia tests compounds to collect a complete set of data regarding their structure, properties, and effects. They combine this information with the existing data from past drug trials, research papers, patent databases, and other sources. The Centaur AI platform then employs machine learning to analyze the results and determine the potential use of compounds against specific diseases and disorders. They also use their Artificial Intelligence platform to mine data to discover what properties a molecule needs to treat a specific condition.
Describing itself as a “full stack AI drug discovery company,” Exscientia collaborates with drug giants such as Bayer and Bristol Myers Squibb. It has one AI-designed drug (DSP-1181) in human clinical trials, designed to treat obsessive-compulsive disorder (OCD). It took only a single year to go from inception to trials. A second drug is almost ready for clinical trials with a similar timeline.
When you consider the vast number of compounds and drugs in existence today, it’s possible that the cure for a lethal disease like Alzheimer’s may be just one AI-fueled analysis away!
Moving Beyond Drug Research
The drug research example shows why many organizations and businesses should augment their staff with machine learning engineers, AI architects, big data specialists, and data scientists. After all, the benefits of machine learning and Artificial Intelligence aren’t limited to pharmaceutical research.
Any industry that relies on research to discover new processes, procedures, and technologies should be employing Artificial Intelligence and machine learning in its everyday operation. The benefits of doing so range from finding hidden ways to increase efficiency, productivity, and cost-effectiveness, developing new products and services that bring entirely unique benefits to customers.
Everything from small businesses to retail to education can benefit from using Artificial Intelligence. It’s not just for designing life-saving drugs.
Additionally, businesses should bolster their Artificial Intelligence and machine learning staff with skills and technologies such as DevOps, cloud computing, project management, and quality management.
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