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5 minutes read

Artificial Intelligence (AI) in the Biotech Industry

By Robert Kazmi
By Robert Kazmi
5 minutes read

AI in biotech is one of the most promising use cases of HiTech technologies. Thanks to the possibilities Artificial Intelligence offers, the biotech industry is redefining how many of its processes are managed, not only in terms of new products but also when it comes to services. This has important implications in terms of innovation and new business models.

We are currently seeing how pharmaceutical companies and other biotech actors use big data, Machine Learning, and even Natural Language Processing techniques to develop new products and services for the industry. Drug development and personalized medicine are some of the most important innovations. 

In this post, we discuss the importance of AI in biotech and how companies are using it to redefine the industry. 

What is Biotech?

Biotech, short for biotechnology, is the use of technology-based on biology. One of the main actors in this field is pharmaceutical companies, although they are not the only ones. 

The biotech field has seen important progress in recent years thanks to advances in biology, but most importantly, thanks to advances in how Artificial Intelligence can help researchers. It has become an indispensable tool for any company in the industry that wishes to stay ahead of competitors.

Big Data and Biology

When it comes to the power of Big Data and data analysis, few people think of using these technologies for the life sciences or the biotech industry. The idea of Artificial Intelligence is still closely linked to robots and other machines. However, in recent years the biotech industry has taken advantage of them to do more than standardize processes. It would not be an overstatement to say that they are developing a new precision medicine that will revolutionize healthcare.

These are some of the most important ways in which Artificial Intelligence is helping pharmaceutical companies:

  • Improved Precision
  • Drug discovery 
  • Personalized medicine
  • Gene Editing 
  • Market platform

Improved Precision

When it comes to drug discovery and manufacturing, bio technicians are often faced with costly and difficult tasks that require a great deal of precision. This problem can be easily solved by using AI tools that can help standardize important activities like performing clinical trials, analyzing chemical components, and reaching important conclusions in a precise and reliable way. This will help reduce inefficiencies throughout the life sciences when it comes to business processes. 

Drug Discovery 

Thanks to large pools of data collected throughout the years, pharmaceutical companies are rethinking not only how certain activities are performed. They are also using data analysis for innovating ways to deliver additional value to their users through new drugs. Thanks to powerful data analysis tools and techniques, companies can understand how drugs are synthesized, helping develop new treatments for all sorts of ailments. 

The use of data analysis for drug development not only helps discover new chemical substances to treat diseases. It also helps reduce the need for clinical trials, helping drugs hit the market faster without losing reliability, like in the case of COVID vaccines.

Personalized Medicine

An important landmark in the drug development process came in 2020 when the AlphaFold team made a revolutionary discovery in terms of how proteins unfold. This sounds like a minor thing, but it has the potential to change medicine as we know it, and it was done with the help of Artificial Intelligence. More specifically, it was achieved with the help of Machine Learning.

This has important implications in terms of personalized medicine and how life sciences are researched. It means that, aside from drug discovery, AI in biotech is also helping understand how diseases develop in humans, and this can, in turn, help understand how to solve them.

Gene Editing

As Artificial Intelligence and Machine Learning tools and techniques are refined, and better models are built using Big Data, pharmaceutical companies will be able to understand the human genome better. This will eventually lead to gene editing, something that is already done with techniques such as CRISPR.

By using this technique, personalized medicine is also one step closer to becoming a reality, even if many clinical trials still need to be done. In theory, it will be possible to identify certain diseases that are encoded in our genes before they manifest. Patients will have the ability to address and treat certain heritable diseases before they occur. 

Market Platform

Although it seems that the power of Artificial Intelligence lies mostly in the use of Machine Learning techniques for scientific discoveries when it comes to the biotech industry, there is also room for one additional business innovation. Thanks to the power of Natural Language Processing, a form of AI that allows computers to process language, companies are helping reduce friction points in the market

The pharmaceutical industry is a complex one, and knowledge is very specialized. To help reduce this barrier, AI can help researchers and companies find what they need easily, reducing barriers, helping patients in the process.

Biotech Software

AI sometimes feels like a magical solution to some of the world’s hardest problems. Because of this, we often forget that, at the end of the day, it is software. Like any other software solution, AI requires a deep understanding of the problems it intends to solve, but it also needs developers that know how to use the right software development tools.

The MedTech industry is a complex one, and it requires specialized development knowledge that guarantees reliable and easy-to-use digital products. Any AI solution for the biotech industry needs to consider this. 

The best alternative for any pharmaceutical company wishing to develop an AI solution for biotech problems is to find a qualified development partner that can build the necessary software solution. 

Failing to do so may result in a low-quality product that can easily throw away millions of dollars invested in life-saving research.

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