The amalgamation of AI and Biotechnology
Written by Harikrishnan M
Artificial intelligence or the so-called ‘AI’ is the new talk of the town. It is being said that AI is the best thing since sliced bread. AI has not left any stone unturned in its path of success, including biotechnology, which is currently in the limelight after the emergence of the pandemic. The industry is facing a huge demand in almost all the verticals. Various companies are searching for opportunities to invest in red-hot innovations, analysis, and storage of elephantine research data and keep an eye on the business’s profitability. For meeting such humungous demands, a vital role is being played by AI-enabled applications [1].
AI: An indispensable aid for Biotechnology
A mind-blowing article, ‘Data Is the New Oil of the Digital Economy’ has proposed the concept of data being an untapped valuable asset, and one who knows this reaps all the benefit. The biotech industry generates a huge amount of data, and the profit relies on the day-to-day storage, analysis, and interpretation of these data. To perform these analyses, every biotechnology company, whether pharmaceutical, sequencing-based, or enzyme production, requires various software tools to minimize manual glitches and sustain this competitive race[2].
The emergence of Covid-19 has created a health emergency, and with the current reforms such as lockdown and reduced workforce, the world economy is on the decline, creating a perfect storm. To prevent the situation from getting out of hand, everyone is fondly anticipating the biotechnology industry to develop more and more highly efficient vaccines. The use of AI in biotechnology can uplift the pace at which the research happens in the industry and boost the current production systems and supply chain networks.
Programs such as H2O.ai and CRISPR libraries handle the monotonous jobs of day-to-day data analysis of various operations such as composition studies and gene editing and analysis for more rapid and precise outcomes. AI-enabled programs are taking over the conventional laboratory analysis, which used to be tedious and time-consuming. This will save time for scientists, who can now focus on more pertinent issues and solutions[3].
AI: A handy tool in Drug Development Cycle
AI-enabled software tools can be used to analyze and interpret the data generated, boost vaccine development by identifying the drug combination and identify the right market, which can further enhance profitability. As said by Melanie Matheu in her article for medium, AI-based screening can accelerate drug discovery with reduced failure rates in clinical trials. A startling article published in BBC News in 2020, ‘One of biology’s biggest mysteries largely solved by AI,’ describes how DeepMind, a London-based AI start-up, used deep learning algorithms to determine protein folding to a 3D form with high precision[4].
AI can also help upgrade the complete drug development cycle until the drug hits the market. AI platforms such as QuartzBio (recently acquired by Precision Medicine Group) focusses on advancing the drug development process by analyzing the clinical data. Based on market analysis, AI can employ tools to speculate the future demand for a particular drug, structure prediction, modification, selection of the right drug combination, and intelligent use of raw ingredients to save precious laboratory time and investor’s money[5].
AI: A companion to farmers and agricultural scientists
Agricultural biotechnology is yet another important area where AI has made an enormous contribution. AI-enabled tools have become inevitable for gene modification tasks, including comparing the crop’s quality and features to predict future yields. AI-based robotics are widely used in the agricultural industry for harvesting and packaging jobs. A perfectly trained AI can predict the yield and quality based on the weather forecast, nature of farmland, quality of seeds, and fertilizer used.
Plantix, an AI-based tool developed by German startup PEAT, claims that it can identify nutrient deficiencies in the soil and give an insight into the pest and diseases that may occur in the future. Similarly, a company called Trace Genomics uses machine learning algorithms to perform soil analysis. Machine learning-based company SkySquirrel Technologies uses Aerial drone images and analyses them to assist farmers in crop monitoring[6].
Conclusion
AI has contributed significantly to general applications and is currently creating seminal innovations in the pharmaceutical and health industries. The rapid advancement of AI suggests that it can come in handy in performing various biotechnology-related jobs from impactful predictions, widening scope, effective decision making, and gaining profitability. Even though AI is being utilized in numerous biotechnology-related applications, its full potential is not being explored. The question is not why AI, but when, where or how to incorporate AI in biotechnology and until we explore AI to its fullest, the answer to these question remain little far-fetched.
Thanks to the Author Harikrishnan M, Reviewers Godwin J, Poornima Ramesh, and the editor Mahesh Velusamy
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References
- Artificial Intelligence in the Biotechnology — 5 Key Trends, 2020, Xcelpros.
- Toonders, J., n.d. Data Is the New Oil of the Digital Economy | WIRED. Wired.
- Catherine Shaffer, 2020. Artificial Intelligence Is Helping Biotech Get Real. Genet. Eng. Biotechnol. News.
- Helen Briggs, 2020. One of biology’s biggest mysteries “largely solved” by AI. BBC News.
- Jan Zawadzki, 2020. Vertical vs. Horizontal AI Startups | by Jan Zawadzki | Towards Data Science. Towar. data Sci.
- JNPRAVAR@GMAIL.COM, 2020. Artificial Intelligence in Agriculture: Using Modern day AI to solve Traditional Farming Problems. Analytics Vidhya.
