AI or Artificial Intelligence can have a transformative impact on biotechnology. There are many ways and many areas where biotech companies can leverage AI and enrich their work.
Biotechnology firms are now realizing the value that AI can bring to their entire business, in the form of –
While PricewaterhouseCoopers estimates that Artificial Intelligence will contribute to the global output with $15.7 trillion by 2030, a survey of pharma and life sciences experts revealed that 44% were using Artificial Intelligence in their R & D activities.
AI in Biotechnology
With the amount of data available to the biotechnology scientists worldwide, it becomes crucial to rely on artificial intelligence and machine learning so that they can parse through the huge data lakes, carry out the data analysis tasks, and productively progress at a faster pace.
Biotechnology can be categorized into a few types like agricultural biotechnology, medical biotechnology, animal biotechnology, industrial biotechnology, and bioinformatics. Let us see how Artificial Intelligence is impacting these branches of biotechnology.
Agricultural biotechnology develops genetically modified plants to increase crop yields or introduce new characteristics to the existing plants. It involves conventional plant breeding, tissue culture, micropropagation, molecular breeding, and genetic engineering of plants.
Biotechnology firms are now leveraging Artificial Intelligence and Machine Learning techniques to develop and program autonomous robots that handle important agricultural tasks like harvesting crops at a much faster pace than humans. Computer Vision and Deep Learning algorithms are leveraged to process and analyze the data captured by the drones. This helps in monitoring crop and soil health. Machine Learning algorithms help in tracking and predicting various environmental changes like the weather changes that impact the crop yield.
Medical biotechnology uses living cells for the betterment of human health by producing drugs and antibiotics. It also involves the study of DNA and genetically manipulates the cells to increase the production of important and beneficial characteristics.
Artificial Intelligence and Machine Learning are extensively used in drug discovery. Machine Learning helps in discovering small molecules that could give therapeutic benefits dependent on known target structures. Machine Learning is widely used in diagnosing diseases as it uses the true result to improve the diagnostic tests i.e., the more diagnostic tests that are run, the more accurate results can be achieved. AI is also helping in reducing the radiation therapy planning process resulting in saving time and improving patient care. Another area where Artificial Intelligence and Machine Learning are proving to be promising include enhancing the EHRs with evidence-based medicines and clinical decision support systems. Apart from the above-mentioned applications, these technologies are widely used in gene editing, radiology, personalized medicine, medication management, etc.
This branch applies molecular biology techniques to genetically engineer/modify the animals to improve their sustainability pharmaceutical, industrial, or agricultural purposes.
Breeding of animals is one area where Artificial Intelligence and machine learning models provide valuable insights. Selective breeding is a very common practice where animals with the most desirable characteristics are bred with each other so that their offspring will also result in the same traits. This practice is implemented on the molecular level too where genetic characteristics among the animals are selected and such animals are bred. Machine Learning helps in understanding the genomics and making informed decisions and enhancing the capabilities of scientists in predicting the expression of those genes.
Industrial biotechnology is all about biopolymers substitutes, the invention in various areas like vehicle parts, fuels, fibers, new chemicals, and the production process.
Internet of Things (IoT), Machine Learning, and Artificial Intelligence analyze the machines, predict outages, optimize equipment, etc to provide efficient production and better product quality. Computer-aided designs and Artificial Intelligence are coming up with the desired molecule design. Robotics and Machine Learning cultivate the strains and test to what extent the desired molecule was reached.
Bioinformatics helps the acquisition, storage, processing, distribution, analysis, and interpretation of biochemical and biological information with the help of mathematical, computer science and biology tools to understand the biological significance of a variety of data. This information is organized in large data pools.
This information needs to be harnessed to gain tremendous insights. Artificial Intelligence and Machine Learning are leveraged in DNA sequencing from the huge data crunch involved, classification of protein along with protein’s catalytic role and biological function, analysis of gene expressions, genome annotation where a certain level of automation is required to identify the locations of genes, computer-aided drug design, etc.
The swift progress of Artificial Intelligence indicates that it can be applied to a large variety of jobs, workflows, and tasks in a Biotechnology company. If your Biotechnology company is exploring on when, where, or how to leverage AI, Qualetics, our on-demand Data Intelligence as a Service platform can help you build quick AI capabilities for your organization.
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