Impact of AI on the Biotechnology Industry - Use Case

 

Artificial Intelligence isn't just a common term in the Information Tech sector now but a revolutionary tool for all sectors worldwide.  AI with Machine learning and its subset of Deep Learning has brought significant advancements and transformative changes.  The latest sector to join the successful sector to adopt AI and reap its benefits is the Biotech Industry. 

This blog highlights the impact of AI in the Biotech Industry while also zeroing in on the latest trends and use cases which has fueled the innovation growth for the future of AI in Biotech. 

What is Biotechnology?

Biotechnology, in simple terms, is a field that uses living organisms or biological parts to create useful products.  Biotechnology combines biological data with computer technology to promote nanotechnology and regenerative medicine research.  Today, multiple commercial biotechnology corporations produce genetically altered molecules for various largely medicinal, agricultural, and environmental applications.

With the intervention of AI, the biotechnology business has evolved tremendously in recent years. Researchers have used AI and machine learning to find the proper information for their experiments. Many organizations have begun to use Artificial Intelligence in Biotechnology to improve their daily operations and discover new prospects. 

 

The Journey of AI-Enabled Biotechnology

Biotechnological data is an abundant resource, potentially producing alternative solutions and innovative products.  The abundance requires computational tools to analyze and diagnose the data, which is where the need for AI arises. 

Early Usage of AI in the Biotech Industry (1960s-1990s):

 

• Expert systems and rule-based AI approaches have helped in biological processes such as protein folding and drug design. 
• Scientists also implemented AI in biotechnology to combat limited computing power and less complex data.

 The Emergence of Machine Learning (the 1990s-2000s):

 

• Machine Learning gained popularity in the biotech industry as computational power increased.
• Machine learning algorithms of decision trees analyze biological data and predict molecule properties. 
• Machine learning has contributed to Biotechnology via drug-target interaction forecast, gene expression analysis, and biomarker discovery.

Deep Learning Revolution (2010s-present): 

Deep learning is a subset of Machine Learning and is popular in the Biotech Industry for its medicinal discovery potential. 

• Extensive datasets and Graphics Processing Units (GPUs) for these sets propelled the start of Deep Learning in Biotechnology. 
• Deep learning models get implemented in image analysis, genomics, proteomics, and drug discovery.
• Reinforcement learning is also gaining popularity for improving experimental designs and determining effective treatment techniques. 

 
Uses of Artificial Intelligence in the Biotechnology Industry: 

Improved Precision: Precision is crucial for bio technicians in the discovery process.  However, errors can be time-consuming and costly.  Thankfully, AI and machine learning can help solve these challenges by standardizing important activities like analyzing chemical protein components, conducting clinical trials, and making decisions based on precise and trusted conclusions.  With these latest technologies, biotech organizations can reduce the scope of error and produce quality results in a shorter period.

Drug Discovery- The discovery of new drugs is an important aspect of biotechnology.  This procedure includes testing genes, peptides, proteins, or other compounds for medicinal potential and pursuing promising candidates.  AI, machine learning, and Deep Learning are necessary to analyze huge datasets and provide insights that can lead to the discovery and production of new drugs for rare diseases.

Gene- Editing - The study of the human genome in Biology is an ongoing process that has evolved over generations.  With Artificial Intelligence in biotechnology, it has become easier to comprehend big data, leading to advancements in gene editing and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat).  This technology allows patients to identify their heritable diseases and improve their treatment before contracting them. 

Animal Biotech: AI in animal biotech has proven to be a valuable tool in molecular biology.  It allows modifying genes and animal traits to create crossbreed versions for agricultural and pharmacological applications.  In addition, AI plays a crucial role in selective breeding by combining animals of a specific type to produce offspring with similar attributes.  Machine learning technology can also efficiently handle large databases with genomic data about various animal types and assist in selecting animals for selective breeding.  

Industrial Biotech:  Industries use Artificial Intelligence and Deep Learning to facilitate end-to-end operations.  AI tools, for example, can generate 3D images of molecules and modify their structures to produce new chemical compositions.  

Agricultural biotechnology: Nowadays, biotech companies are utilizing Artificial Intelligence and Machine Learning to create independent robots that can perform agricultural tasks, like crop harvesting, more efficiently than human workers.  By implementing Deep Learning algorithms, data scientists can analyze crop fields, and drones can collect data to monitor crops and soil health.

Forest Biotechnology: Forests are an essential part of our ecosystem and a valuable human resource.  However, deforestation has led to the loss of these resources over time.  Organizations are combining biotechnology and AI to combat this to increase forest sustainability.  This approach is crucial for meeting the global demand for sustainable forest plantations.

Artificial intelligence is helping to foster innovation in the biotech industry from the laboratory to the market.  AI-powered technologies and apps are aiding in developing molecular structures that meet market demands throughout a medicine or chemical compound's lifecycle.

Conclusion 

Now is the time for biotech companies to be more active and choose advanced AI technologies for their business to boost efficiency and results.  Companies need to speed up their research and bring forth never-heard-before inventions to stay ahead of their competitors.  Biotech firms are revamping their usual process by patterning with AI service providers with the help of Machine Learning (ML) and Artificial Intelligence (AI).  

 Are you looking for an AI solution to resolve your biotech challenges? 

Contact Calpion to know how our customized AI solutions have helped our biotech clients to improve their productivity and contributed to the discovery and development of drugs and clinical trials.