Why is artificial intelligence critical to biotechnology?

Biotechnology lies somewhere in the middle between biology and technology. Through modern technology, it uses biological processes, organisms, cells, molecules and systems to create new products that benefit people and the planet. In addition, it encompasses laboratory research and development, exploring and extracting biomass through bioinformatics, and developing high-value products through biochemical engineering. Biotechnology is widely used in various fields such as agriculture, medical care, animals, and industry.

White biotechnology is concerned with using biomass to make products that require chemical processes, and can also address the energy crisis by producing biofuels, which can be used for vehicles or heating.

Every organization working in the biotechnology field maintains large data sets stored in databases. This data must also be filtered and analyzed to be valid and applicable. Operations such as drug manufacturing, chemical analysis, enzyme research, and other biological processes should be supported by computerized physical tools to achieve high performance and accuracy and help reduce manual errors.

Artificial intelligence is one of the most useful technologies to help manage biological processes, drug production, supply chains and process biotech data.

It interacts with data received through scientific literature and clinical data trials. AI also manages incomparable clinical trial data sets and enables virtual screening and analysis of large amounts of data. As a result, it reduces clinical trial costs and brings discoveries and insights to any area of ​​biotech operations.

More predictable data makes it easier to build workflows and operations, improves execution speed and program accuracy, and makes decision-making more efficient. 79% claim that AI technology will impact workflow and become key to productivity.

All of this results in a more cost-effective solution. Over the past three years, estimated revenue generated with the help of AI has grown by $1.2 trillion.

Advantages of using artificial intelligence in biotechnology

Artificial intelligence has applications in various fields, but the most important one is the application of artificial intelligence in the medical field. While the ability to use techniques such as data classification and conducting predictive analytics is beneficial to any scientific field.

Manage and Analyze Data

Scientific data continues to expand and must be arranged in a meaningful way. The process is complex and time-consuming: scientists must complete repetitive and laborious tasks that must be taken seriously.

The data they use is an essential part of the research process, and failure can result in high costs and energy losses. Furthermore, many studies do not produce practical solutions because they cannot be translated into human language. Artificial intelligence programs help in the automation of data maintenance and analysis. The AI-powered open source platform helps reduce the repetitive, manual and time-consuming tasks lab workers must perform, allowing them to focus on innovation-driven operations.

Thoroughly examine genetic modifications, chemical compositions, pharmacology studies, and other critical informatics tasks for shorter, more reliable results.

Effective data maintenance is indeed critical to every scientific department. However, the most significant advantage of AI is its ability to organize and systematize data into forms and produce predictable results.

Driving Innovation in Healthcare

Over the past decade, we have faced challenges in the manufacturing and deployment of pharmaceuticals, industrial chemicals, food-grade chemicals, and other biochemistry-related raw materials. innovation needs.

Artificial intelligence in biotechnology is critical to promoting innovation throughout the life cycle of a drug or compound and in the laboratory.

It helps find the right combination of chemicals by calculating permutations and combinations of different compounds without the need for manual laboratory testing. Additionally, cloud computing makes the distribution of raw materials used in biotechnology more efficient.

In 2021, research lab DeepMind used AI to develop the most comprehensive human protein map. Proteins perform a variety of tasks in the body—from building tissues to conquering disease. Their molecular structure determines their use, which can be carried out in thousands of iterations - understanding how proteins fold can help understand their function so that scientists can figure out many biological processes, such as how the human body works or create new treatments and drugs.

Such platforms provide scientists around the world with access to data about discoveries.

Artificial intelligence tools help decode data to reveal the mechanisms of specific diseases in different regions and help accurately adapt analytical models to their geographical locations. Before the use of AI, time-consuming and expensive experiments were required to determine the structure of a protein. Today, some 180,000 protein structures produced by the program are freely available to scientists through the Protein Data Bank.

Machine learning helps make line diagnostics more accurate, using real-world findings to enhance diagnostic testing. And the more tests you perform, the more precise the results you generate.

AI is a great tool to enhance electronic health records with evidence-based medication and clinical decision support systems.

Artificial intelligence is also often used in genetic manipulation, radiology, customized medicine, drug management and other fields. For example, according to current research, AI improves the accuracy and efficiency of breast cancer screening compared with standard breast radiologists. And another study claims that neural networks can detect lung cancer faster than trained radiologists. Another application of AI is the more accurate detection of disease through X-rays, MRI and CT scans through AI-driven software.

Reduce research time

Due to globalization, new diseases are spreading rapidly across countries. We witnessed it with COVID-19; therefore, biotechnology must accelerate the production of necessary drugs and vaccines to protect against such diseases.

Artificial intelligence and machine learning maintain the process of detecting appropriate compounds, assist in laboratory synthesis, help analyze the validity of data, and provide it to the market. Using artificial intelligence in biotech reduces operational performance time from 5-10 years to 2-3 years.

Improving Harvest Yields

Biotechnology is critical to genetically engineering plants to produce richer harvests. The role of AI-based technologies in studying crop characteristics, comparing quality and predicting real-world yields is increasing. Agricultural biotechnology also uses robotics, a branch of artificial intelligence, to perform manufacturing, collection and other critical tasks.

By combining data such as weather forecasts, agricultural characteristics and the availability of seeds, compost and chemicals, artificial intelligence can help plan future material recycling patterns.

Artificial Intelligence in Industrial Biotechnology

The Internet of Things and artificial intelligence are widely used to produce vehicles, fuels, fibers and chemicals. Artificial intelligence analyzes data collected by IoT and transforms it into valuable data to improve production processes and product quality by predicting results.

Computer simulations and artificial intelligence suggest expected molecular designs. Strains are being produced through robotics and machine learning to test the accuracy of developing the desired molecules.

Summary

Although this is just the beginning of the use of artificial intelligence in biotechnology, it can already provide many improvements in various fields. Furthermore, the continued development of artificial intelligence in biotechnology demonstrates that it can be used in a variety of processes, operations, and strategies to gain a competitive advantage.

Not only drives innovation, but is a valuable tool that reduces costs by conducting more accurate tests and predicting results without the need for actual experiments in the lab.

In addition to discovering humanity’s future necessities in healthcare and agriculture, predicting potential losses, and making predictions for businesses, they should direct resources toward more efficient production and supply.

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