How AI is Revolutionizing Drug Discovery and Development 

The pharmaceutical sector is experiencing a massive shift. It’s a shift that traditional laboratories aren’t powering. Instead, it’s artificial intelligence that’s driving this shift.  

In the past, drug discovery and development took several clinical trials, billions of dollars, and years to achieve. But AI has made it a breeze, helping to analyze massive data, identify patterns, and possible drug interactions before kick-starting a clinical trial.  

While artificial intelligence is spearheading a massive shift in the pharmaceutical sector, the industry is still struggling to harness its full potential. This is where healthcare AI consulting comes in, helping research and pharmaceutical agencies harness the full potential of artificial intelligence. The outcome is lower research cost, timely production of crucial pharmaceuticals, and better outcomes for patients.  

Here, we will discuss how AI is transforming the drug industry. Read for more information on this topic.  

The Challenges Pharmaceutical Industries Face In Drug Discovery 

For every drug you find on the shelves of pharmaceutical stores, tons of clinical trials, failures, and billions of dollars had been invested. Drugs are one of the most complex and time-consuming inventions. They take several years and massive investments.  

A pharmaceutical company could spend over $2.5 billion to create a new drug. It could also take between 10 and 15 years to create a new drug, from discovery to clinical trials, and final product.  

Research and pharmaceutical agencies may even abandon most drug development projects after several failed attempts. That means years of research and billions of dollars invested, all gone. 

Here are the challenges pharmaceutical and research agencies face while using the traditional drug development strategies.  

1.  Massive data: Pharmaceutical companies handle a massive amount of data during drug development. Activities such as gnome sequences, and chemical structures of the compounds used generate massive data. Drug development also involves several clinical trials which generate a massive amount of data. There is a possibility of human errors when manually analyzing and identifying patterns in such massive data.  
2. Increased attrition rates: Drugs in clinical trials can fail after years of research, development, and billions of dollars spent. Most drugs fail because of their inefficacy. In some cases, the side effects can make a drug unfit for release into the market.  
3.  Time consuming clinical trials:  Clinical trials are expensive and take time. It takes time to recruit people, study, and analyze the results of the trials. This process alone could take years and enormous resources. 
4. Rising cost of research and development: The rising costs of conducting research and development of drugs could put research and pharmaceutical agencies under unwanted pressure to deliver more with dwindling resources.  

How AI Is Revolutionizing Drug Discovery and Development  

We have explained the challenges research and pharmaceutical agencies face in drug development. The problems range from handling massive data sets to high attrition, time-consuming clinical trials, and rising costs.  

The introduction of artificial intelligence in drug research and development is changing the game. AI is making it possible for companies to produce drugs faster and at a lower cost. How is it doing it? Here are some of the ways AI is shaping the drug discovery and development process.  

Identifying and validating biological targets: 

 Drug discovery and development happens in stages. The first stage involves finding and validating a biological target. The target could be a gene or protein that is fueling disease progression.  

Identifying biological targets isn’t an easy process. It could take years of laboratory research with considerable resources.  

The introduction of artificial intelligence has made this process significantly faster and less expensive for the pharmaceutical industry. Here is what AI is doing to accelerate the process: 

• Analyze genomic data using machine learning to identify biological markers or potential mutations responsible for disease progression. 

• Alteration of biological pathways to determine if making slight changes to a biological target can help in disease treatment and cure.  

Screen and optimize compounds: 

The first step in drug discovery and development is the identification of a biological target. After that, researchers have to create a compound that can successfully interact with it.  

This process can take years and plenty of resources, too. Why? The researchers have to screen millions of biological molecules in the laboratory to find the right one.  

Artificial intelligence is handling this stage of drug development more efficiently. Here is how it’s doing it: 

• Deep learning algorithms analyze chemical libraries in silico with the hope of figuring out the molecules that can successfully bind.  

•  Pharmaceutical companies use artificial intelligence to create new designs of the identified molecules rather than relying on existing chemical libraries.  

• AI algorithms identify potential toxicity issues even before the drug enters preclinical trials.  

 Speed up preclinical trials: 

Pharmaceutical companies spend huge resources during preclinical trials. Aside from the vast resources, preclinical trials take a lot of time.  

Though this stage involves using the drug on animals, researchers have to evaluate the performance of the drug on the subject.  

How is artificial intelligence speeding up preclinical trials, saving pharmaceutical companies time and money? 

• There are computational models capable of simulating how a drug would interact with biological systems, ending the need for preclinical trials using animal subjects. 

• Machine learning algorithms have the potential to identify potential side effects, reducing the possibility of failure in drug production.  

• Artificial intelligence can analyze MRI scans with far greater accuracy than humans. 

 Faster and more efficient clinical trials: 

Pharmaceutical companies face several challenges during clinical trials. They sometimes struggle to find the ideal subject for the trials and work with incomplete data. But artificial intelligence has come to address these challenges, and here is how it’s doing it: 

• Artificial intelligence is helping pharmaceutical companies recruit the ideal subjects for their clinical trials. AI scans health electronic data to find subjects eligible for the trial.  

• Artificial intelligence predicts outcomes of trials, helping researchers to create and follow a more effective trial protocol.  

Conclusion 

You have read how AI is revolutionizing drug discovery and development. Artificial intelligence is helping research and pharmaceutical companies save billions of dollars in drug research and development. 

AI isn’t only helping pharmaceutical companies save money. It’s also helping them to save time and create more effective drugs. It is making drugs more readily available, saving lives, and helping people live healthier.