Generative AI in Pharma — Accelerating Drug Discovery
Published: 2024-05-16 10:02:30

With Generative AI being actively used in the process of making different medicines, the pharmaceutical business is going through a big change. As a rule, it takes between 12 and 18 years to make a pharma product, and the costs can go sky-high — up to $2.6 billion. 

Still, there is no guarantee that the medicine will be approved by the FDA and will reach the market — only about 10% of applicants make it to the trials. However, AI is changing the way things are done and making it faster and more effective to make new drugs.

## Revolution in the Pharma Industry Happening Right Here, Right Now
The pharmaceutical industry is currently undergoing a massive revolution that is set to change the way we approach healthcare. The use of AI in the pharma industry is transforming the way research and development is conducted, and it is also changing the way drugs are manufactured and marketed. The thing is that [generative AI solutions in drug discovery](https://www.griddynamics.com/blog/generative-ai-drug-development) reduce the time it takes to bring new drugs to market and improve patient outcomes. Here are some of the most prominent use cases.

###1 — Keeping Molecular Design and Discovery Moving Quickly
Generative AI is a true game changer when it comes to finding possible drug options because it speeds up molecule design and discovery. It's used to make new chemical structures that fight certain illnesses. AI speeds up the normally slow process of drug finding by making molecules that are better at working and being safe. This technology makes it possible to develop new pharma products to treat diseases that couldn't be treated before.

###2 — Changing Drug Ingredients to Get Better Results
Once a good drug option is found, AI helps make it better by improving its structure. The latter is changed in this process to make the pharma product better. AI-generated chemicals are synthesized and tested, which improves their medicinal properties. This approach allows for the development of safer and more effective treatments.

###3 — Repurposing Existing Pharma Products for New Therapeutic Use
Reusing old drugs in new healing ways is another important part of drug reuse that AI plays a big part in. It includes changing drugs so that they can treat different health problems. By making molecules that are like known drugs but have different traits, AI makes it possible to find new ways that current medicines can help people get better.

###4 — Streamlining Experiment Documentation and Analysis
Pharmaceutical companies deal with huge amounts of data from tests that are still being done on new drugs. Generative AI makes this easier by tracking trials automatically. It makes short notes and recaps. Thus, keeping records is way easier and more efficient. These AI-generated papers make important information easy to find and help people make smart decisions.

###5 — Introducing Cutting-Edge Solutions for Screening and Formulation
Artificial intelligence doesn't just make molecules; it also changes how drugs are made and how they are screened virtually. AI predicts the binding affinity of compounds, giving priority to those with the target cellular function. This approach speeds up the process of drug development, especially for complex disorders.

## AI Use Cases in the Pharma Industry
Generative AI changes the way drugs are found, cutting years of study down to months. The technology opens the door to new treatments much faster than ever before. And many companies are actively using all the benefits and opportunities that have become a reality with artificial intelligence.

### Insilico Medicine
With its Gen AI platform, Pharma.AI, Insilico Medicine has reached a major milestone. Their drug INS018_055 (made by AI) is now in [Phase II clinical testing](https://insilico.com/blog/first_phase2) with people. It is made to treat idiopathic pulmonary fibrosis, a rare lung disease that makes breathing hard. It took about three years to get to Phase II, instead of the usual 12 to 18 years. 

### Adaptyv Bio
With its AI and synthetic biology tool, Adaptyv Bio is making protein engineering better. By mixing advanced robots and microfluidics, the technology makes it easy to test protein designs that are based on AI. Their creative method is very important for making new medicines and sustainable materials.

### Iktos and Curreio
Iktos and Curreio work together to use cryo-EM for AI-driven drug creation. The goal of their agreement is to speed up the creation of new experimental drug candidates. They want to improve the process of finding new drugs by using AI to find molecules that meet a number of important criteria and cryo-EM to look at molecules' structures in great detail.

### Sanofi
Generative AI is being actively used by Sanofi in its manufacturing and supply processes. The company has digitalized quality control, which makes it more accurate and useful. By combining these technologies, the company has been able to increase its output, which means it uses its raw materials more efficiently. Notably, Sanofi can now correctly predict 80% of low stocking spots thanks to AI. It makes the production line much more reliable and efficient.

## Final Say!
The use of generative AI in the pharmaceutical industry has revolutionized the way drugs are developed. The benefits of generative AI in the pharmaceutical industry are not limited to academics and researchers. The technology also has a significant impact on the future of health. By speeding up drug development, generative AI can help bring life-saving drugs to market faster. 

This can have a profound impact on patients who are suffering from life-threatening diseases or conditions. Additionally, the technology can help reduce the cost of drug development, making it more accessible to people who may not have access to expensive treatments. [AI has been actively used in software development](https://dev.to/loganmary689/ai-software-development-lifecycle-3hhn) and has opened a lot of opportunities; the same future is predicted for the pharma industry.