Artificial Intelligence in Biotechnology | How AI is revolutionizing Biotech
Biotechnology has taken modern society by storm. From its crucial role in agriculture, medicine, genomics, immunology, and other scientific and non-scientific fields, to its vital contributions in the race to discover the COVID-19 vaccine, biotechnology permeates almost every aspect of society. The biotech industry, or the broad area of biology involving the use of living systems to develop products using technology, is growing at an unprecedented pace in modern society. In fact, according to a new report, global biotech drug sales generated 290 billion dollars in 2020 and the market size is predicted to reach 2.44 trillion dollars by 2028.
This massive growth is thanks to the explosion of big data in the last few decades that allows biotech scientists throughout the world to gain valuable insights, analyze new trends, and make informed predictions. But as the amount of data, as well as the dire need to utilize this data for the benefit of humanity increases exponentially, it has become critical for scientists to successfully harness this vast amount of structured and unstructured data to perform drug manufacturing, sequencing of DNA and RNA, chemical analysis, etc. at a rapid pace and without any manual errors. So how can this indispensable industry continue to keep up with the surge of big data and latest advancements? Well, this is where Artificial Intelligence comes into play.
Revolutionary AI software and Machine Learning algorithms play a transformative role in effectively harnessing the plethora of data available in this industry and in enhancing productivity, speed, and power for a diverse spectrum of operations.
The biotechnology industry has several subsectors, namely agricultural biotech, animal biotech, industrial biotech, and medical biotech, that skillfully leverage the advancements of Artificial Intelligence and Machine Learning technology.
Agricultural biotechnology serves to develop genetically modified plants to increase crop yields and introduce desired characteristics in them. This subfield uses AI techniques to program autonomous robots to harvest crops at a much faster rate than humans, develop Computer Vision algorithms to process and analyze data captured by drones to monitor crop and soil health, and deploy Machine Learning software to track and predict environmental changes that impact crop yield.
Animal biotechnology is a branch that applies molecular bio techniques to alter animals’ genes to improve their sustainability for industrial or agricultural purposes. Selective breeding at a molecular level is where favorable genetic characteristics among animals are selected and these animals are bred. Machine Learning predicts the expressions of these genes, thus helping scientists to make informed decisions when performing artificial selection.
Industrial biotech involves the production of new enzyme catalysts to maximize and optimize biochemical pathways that can be used in manufacturing. Computer-aided designs and AI are helping generate accurate molecule design, and machine learning helps calculate permutations and combinations of different chemicals to reveal an accurate formula without having to conduct lengthy manual experiments in the lab. Operations that usually take 5-10 years now only take a couple at most with the use of artificial intelligence in biotechnology.
Medical biotech produces drugs and antibiotics to better human health and genetically manipulates cells to produce beneficial characteristics. Machine Learning and Computer Vision are commonly used in the detection of cancers and other diseases, as it makes the identification more precise by improving upon itself every time the algorithm is run. And this personalized medicine is a valuable aspect of AI technology in the medical field.
It is an exciting time ahead for the wonderful world of biotechnology with the power of Artificial Intelligence. The future of biotech is indeed bright!
TIMESTAMP
00:00 Introduction to Biotechnology
00:10 Biotech Fields
00:30 Size of Biotechnology market and drug sales volume
00:47 Big Data in Biotechnology
01:14 Structured and Unstructured Data in Biotech
01:30 Artificial Intelligence in Biotechnology
01:55 Sectors of Biotechnology - Agriculture, Animal, Industrial, Medical
02:13 Agricultural Biotech and AI/ML
02:45 Animal Biotech and Artificial Intelligence/Machine Learning
03:15 Industrial Biotech and Computer Aided Designs
04:00 Medical Biotech, Drug Design using AI, Gene Editing
04:42 Convolutional Neural Networks
05:05 Personalized Medicine using AI/ML
05:22 Radiology and AI
05:50 Revenue Projection in Biotech using AI
06:00 AtomWise and CNN
06:16 EyeNuk and Diabetic Retinopathy
06:36 Desktop Genetics Gene Editing
06:45 DeepMind AlphaFold
07:00 Artificial Intelligence Revolutionizing Biotechnology
07:28 AI meets Biotech
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