March 3, 2024

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How Is AI Revolutionizing Drug Discovery? 

AI drug discovery is helping researchers develop life-saving treatments with less time, money, and resources. Pharmaceutical researchers use AI to assist in many tasks, like drug testing and data analysis. The use of AI in the drug development process is drastically reducing the time it takes to identify new treatments and get them to market. How does AI drug discovery work, and how is it changing the industry?

How AI Drug Discovery Works

This technology has existed for several years but only recently became widely recognized. A team of Australian researchers from Flinders University announced the first AI-developed vaccine in 2019. Just a year later, in 2020, AI was instrumental in creating life-saving COVID-19 vaccines.

What makes AI such a revolutionary tool for developing vaccines like this? Algorithms have specific strengths, including pattern recognition and repetitive process automation. Both are invaluable in drug discovery, particularly in the early stages.

When researchers try to identify new treatments, they must screen thousands of possible combinations of chemicals, compounds, and proteins. Pinpointing a few promising drug candidates using conventional processes can take months or years. Even then, researchers are likely to miss many possible treatments that do not show obvious signs of potential.

Fully Automated Initial Screening

AI can automate this initial screening process completely. Machine learning models trained to comprehend the relationships between different chemical compounds can rapidly analyze thousands of possible drugs.

This is far more efficient than conventional methods — a major benefit for pharmaceutical companies and researchers. 40% of executives report operational efficiency as one of the top benefits of digital transformation, which includes adopting AI, robotics, and similar technologies.

Not only is AI drug discovery significantly faster, but it can also reveal potential treatments that may have gone unnoticed. This is due to the way AI is approaching drug discovery. Algorithms analyze potential drugs from a data-first perspective, searching for numerical trends, patterns, and connections. Using this approach, the AI identifies a collection of promising potential drugs a human researcher can screen.

AI-Powered Simulations and Modeling

After identifying potential treatments, AI continues to aid in drug discovery. It is an excellent tool for modeling and testing new drugs in a virtual environment. Digital modeling and simulations help researchers better understand the behavior of a new drug candidate before developing it for clinical trials.

AI neural networks can simulate new drugs’ reactions with the human body in a risk-free way. The AI models many possibilities simultaneously, highlighting how the medicine might behave, possible complications, long-term effects, and potential side effects. Using AI in this preliminary testing process has a few key benefits.

First, a well-developed AI simulation model can minimize the need for animal testing. Since the AI will likely identify harmful side effects of a new drug candidate, researchers can abandon dangerous ones before the animal testing phase begins. With AI testing, drugs are effectively further along in the discovery and development process by the time they reach the animal testing phase.

Advanced enough AI testing can even eliminate the need for animal testing. The FDA Modernization Act 2.0 — passed in December 2022 — allows computer modeling to replace animal testing for new drugs. So, adopting AI in the drug discovery process can make new life-saving medical treatments safer and more ethical.

Additionally, AI simulations allow researchers to identify risky drug candidates sooner. Researchers can save time and money by pinpointing the most promising candidates and filtering out those with high risk or poor potential. Developing any drug can cost hundreds of millions of dollars, so making the process more time and cost-efficient can have a valuable ripple effect.

Analyzing Clinical Trial Data

Clinical trials are the last big hurdle before researchers get authorization to release a new drug or vaccine. The benefits of AI drug discovery extend to this home stretch of the development process. Researchers can use AI and machine learning to analyze clinical trial data and make the trial process more efficient.

For example, researchers must carefully select candidates for clinical trials, but AI can make the process easier. With the help of natural language processing, an AI model can analyze every candidate’s application. If a candidate has any pre-existing medical conditions or other important traits, the AI can highlight them or flag the application. This helps researchers identify promising candidates more quickly.

Similarly, researchers can use AI to identify insights from trial results and patient data. AI pattern recognition can quickly extract critical trends and connections in clinical trial data, helping researchers understand their effects more efficiently. Adopting AI-powered data analytics may reveal important traits in clinical trial data that would otherwise have gone unnoticed.

Managing Drug Documentation

An important part of drug discovery and development is thorough documentation. This ensures the research behind the development process can be thoroughly peer-reviewed. Documentation is also a crucial part of the patent process and FDA approval.

Managing the extensive paperwork involved in drug development can take much work. AI allows research teams to minimize time spent on paperwork without compromising safety or compliance. Generative AI models — such as ChatGPT — can automate many mundane bookkeeping tasks, such as drafting emails, summarizing research, or writing grant applications.

AI can also help make drug documentation more accessible by automating the translation process. Modern natural language processing models can translate between today’s most popular languages.

For example, in 2022, Meta announced a groundbreaking AI model that can translate between 200 unique languages, including many that lack extensive written content. Algorithms like these allow researchers to share their knowledge and documentation with colleagues, no matter what language they speak.

Advancing Medicine With AI Drug Discovery

AI drug discovery and development is more efficient, thorough, and insightful than conventional processes. Researchers are using AI to identify possible treatments sooner, develop them faster, and understand drug performance in more detail.

AI can also help researchers automate key tasks like data analysis and documentation management, including translating research into other languages. These benefits make AI a game-changing innovation in the pharmaceutical industry.

By Emily Newton

By Emily Newton

Emily Newton is a tech journalist. As Editor-in-Chief of Revolutionized, she regularly covers science and technology stories. Subscribe to Innovation & Tech Today to read more from Emily.

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