The role of AI in the biotechnology industry

  • Applications of AI in biotechnology
  • The role of AI in different biotechnology branches
    • Agriculture
    • Medicine
    • Bioinformatics
    • Industrial Processing
    • Pharmaceutical Industry
    • Environment
    • Animal Breeding
  • Positive effects of AI on the biotechnology industry

Whether you know it or not, we are surrounded by Artificial Intelligence. Almost every technology we use relies on AI in some form. Let’s take a simple example. What do you do when you need more information about something? You search for it online. Search engines use complex AI algorithms to deliver results. AI is also behind the personalized ads you see everywhere on social media. Artificial intelligence improves GPS navigation and enriches it with satellite imagery. Google’s detailed street maps provide details about sidewalks, crosswalks, and pedestrian islands.

Artificial Intelligence has penetrated every commercial industry, and biotechnology is no exception. The global biotechnology market encompasses agriculture, medicine, bioinformatics, industrial processing, animal breeding, environment, and healthcare.


Applications of AI in Biotechnology

The intersection of technology and biology is a powerful place for any biotech company in the 21st century. Informatics helps in analyzing and storing biological data. Machine learning assists scientists in decoding data to understand diseases occurring on the other side of the world. Robotics designs machines useful for packaging and harvesting in the agricultural sector.

Biotechnology has progressed significantly in the past few years. It has combined with artificial intelligence and pushed the boundaries of advancements in pharmaceutical, industrial, and other fields. AI is critical in managing unique datasets, breaking down massive amounts of data, and making accurate predictions.

Deep learning is a type of AI and machine learning that automates predictive analysis. Simply put, it uses a neural network that simulates the behavior of the human brain. Deep learning can recognize patterns and predict how genetic variation can alter cellular processes. It can identify molecules and their effect on specific proteins.


The role of AI in different biotechnology branches

  1. Agriculture

Biotechnology is used to develop genetically-enhanced plants. It means the crop yield will be better and more resistant to pests. It will encourage environment-friendly agricultural practices. Resilient crops require pesticides in lower volumes. It, in turn, leads to better soil health and lesser water pollution. Crops can be modified to increase their nutritional value and provide enough food in times of crisis.

AI and machine learning are used to develop robots that automate harvesting and perform tasks faster than humans. The algorithms track changes in weather and predict their impact on the crops. Deep learning techniques are applied to computer vision to analyze drone images and evaluate crop yield.

  1. Medicine

Biotechnology genetically manipulates living cells to replace defects with healthy characteristics. It is involved in the study of human DNA. Understanding a person’s genetic composition is useful in identifying cancerous cells and how they can multiply in the body. Predicting a population’s disposition to illnesses helps make better diagnoses and start treatments specific to the genetic traits.

AI is especially used in sequencing RNA and DNA and analyzing the chemical composition of different compounds. Machine learning algorithms are prevalent in diagnosing diseases, gene editing, and developing personalized medicine.

Machine learning takes place by repetition. The machine is fed with as much information as possible to teach it the rules of every process. The more diagnostic tests run, the more accurate the algorithm will be.

  1. Bioinformatics

Bioinformatics involves storing, processing, and analyzing biological information. It uses computing, biology, statistics, and mathematical techniques to crunch the data. Bioinformatics focuses on DNA and gene data sequencing, coding, modification, and structure analysis. The basic applications involve analyzing biological sequence and molecular structure, while advanced applications include modeling biological systems.

Computational biology has evolved to produce techniques that play a pivotal role in clinical research. Techniques such as deep learning simplify complex datasets and extract valuable insights. AI is crucial in analyzing gene expressions and genome annotation. It is indispensable in classifying proteins and their role as catalysts.

  1. Industrial Processing

While the petrochemical industry uses hydrocarbons and other chemical compounds, industrial biotechnology uses biological catalysts. These biocatalysts are enzymes that enhance the efficiency of the process and reduce the environmental impact. Bioplastics are plant-based products and are increasingly used in disposable items to curb plastic pollution. Biopolymers are replacing synthetic polymers to reduce the dependence on fossil fuels. Biopolymers are fully degradable macromolecules.

Artificial intelligence assists in creating, testing, and optimizing the desired molecule design. Researchers at the University of Chicago built a database to learn polymer sequences. It means scientists can use it to train a neural network and predict polymer properties. The database can be used to develop new plastics that degrade quickly.


  1. Pharmaceutical Industry

As the world becomes smaller, it becomes easier for new diseases to spread rapidly. Developing new drugs means waiting up to 10 years to receive approval and certification. Human trials make the process costlier and more time-consuming.

On the contrary, AI takes only 2-3 years to develop new drugs and vaccines. AI and machine learning techniques are used to simulate the effect of medicines on the human body and generate safer drug molecules.

The Real-Time Oncology Review conducted by the US Food and Drug Administration is a program that sped up the drug testing and review process using biotech data. The data accurately predicted the outcomes of novel cancer treatment trials.


  1. Environment

Biotechnology is used to make the environment more sustainable. The French company, Carbios uses microbial enzymes to break down and recycle the plastic PET commonly used in plastic bottles. Other organizations have developed genetically modified enzymes for detergents. These new types of detergent are completely biodegradable and remove grease and other tough stains.

IBM uses machine learning to accelerate the development of enzymes for greener industrial processes. The AI model is trained with data on enzymatic biocatalysis from the US Patent and Trademark Office. The datasets are used to increase the model’s accuracy and predictive capabilities.


  1. Animal Breeding

Selective animal breeding is a practice where animals with the best characteristics are bred with each other to produce an offspring with the same traits. Biotechnology is used on a molecular level to select and breed the desired genetic characteristics.

Deep learning uses artificial neural networks that deconstruct a problem and integrate the information. As large amounts of data are fed into the machine, the system learns by itself and improves its decision-making accuracy. Machine learning provides valuable insights as it analyzes genomic datasets and annotates genome sequences.

Positive effects of AI on the biotechnology industry

Artificial Intelligence is making a huge contribution to the global technology industry by mimicking human decision-making processes. Biotech companies have realized the impact of AI in biotechnology and have increasingly invested in AI tools to:

  • Analyze enormous research databases
  • Accelerate innovation and manufacturing
  • Systematically organize the data

Scientists are using AI programs to eliminate the tedious tasks of data entry and maintenance. Open-source AI programs store data on new drugs and the latest industrial developments. They give researchers access to crucial information regarding gene editing and enzyme compositions. By increasing the accuracy of scientific models, scientists can focus better on innovation-driven processes. If executed well, AI has the potential to revolutionize the biotechnology industry and address most of the world’s problems.


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