Artificial Intelligence, ML, and Deep Learning are evolving industries across the globe. They are transforming the way human beings work and live. Artificial Intelligence (AI) is a great addition to the technological world to have a progressive future. AI implementation can make any sector successful, including Biotech. But before we get into the impact AI, Machine Learning, and Deep Learning are creating, let's find out what biotechnology/biotech is.
Biotechnology is a field that uses living organisms or biological parts to create usable products. Biotech comprises the following sectors – bioinformatics, agricultural, animal, medical, and industrial biotechnology. It is a science-based technology to support biomolecular processes to produce desired products.
Artificial Intelligence (AI) and Machine Learning (ML) are expanding their presence in the biotech industry, where they can contribute to the production of drugs and clinical trials to introduce best medicines practices in a shorter period. AI has a transformative impact on the biotechnology industry already. AI-enabled apps in Biotech can perform the following services – drug identification, drug screening, predictive modeling, image screening, drug trial management, and more. AI-integrated apps in Biotech can also perform drug target identification.
Biotech companies use the latest AI technologies to extract meaningful insights from billions of drugs and protein structure experiments to predict the right molecular structure for the best results.
The biotechnology industry has evolved immensely in recent years with the intervention of AI. Researchers have adopted AI and machine learning to find the right information for their experiments. Many organizations have started leveraging Artificial Intelligence in Biotechnology to enrich their everyday functioning and explore new opportunities. AI has become an intrinsic tool for the biotech industry to stay at the top of its growth.
The journey of Artificial Intelligence in Biotechnology began with the large availability of biological data and the need for computational tools to analyze and diagnose this data.
Here's a brief emergence history of AI in biotech industry:
Early Usage of AI in Biotech Industry (1960s-1990s):
The emergence of Machine Learning (the 1990s-2000s):
Deep Learning Revolution (2010s-present):
Overall, Artificial Intelligence and Machine Learning in the biotech industry are progressing in computing power, the availability of large-scale biological datasets, and the need for efficient analysis and interpretation of complex biological information. AI in biotech industries has the potential to significantly impact drug discovery, personalized medicine, and various other branches of biotech research and development.
"Many diseases today don't have a cure. One reason is that drug discovery is difficult: finding and developing an effective medicine is a years long and very expensive process. But maybe it doesn't have to be. Experts say AI—if properly integrated into scientists' research—could revolutionize drug discovery, making it possible for more patients to get the treatments they need." – McKinsey.
Encourage Innovation - Lab to Market: In the last decade, the medical and biotechnology sector has witnessed the need for rapid innovation, production, and supply of medicines, food-grade chemicals, and other raw materials. Artificial Intelligence in Biotechnology plays a pivotal role in encouraging innovation right from laboratories till the end of the biotech lifecycle of a medicine or chemical compound (until it reaches the marketplace). Artificial Intelligence-based tools and applications help develop the molecules' structure based on market requirements.
The dependency on Artificial Intelligence in Biotechnology is increasing and helping in predictive analysis to predict the demand for a particular medicine or chemical in the market. Machine Learning and Deep Learning help in measuring various chemicals to know the correct combination without undergoing different stages of experiments in the lab through manual methods. AI in Biotech companies also permits the smart distribution of raw materials the biotechnology industry needs using cloud computing.
Open-Source AI Platform: Faster Data Research and Analysis: Scientists and researchers across the globe are seeking guidance in AI programs that can help with tedious tasks like data maintenance and data analysis. Crucial tasks like gene editing, chemical studies, enzyme compositions, and more are analyzed systematically for precise and accurate results. Open-source AI programs play an important role by leveraging mundane tasks like data entry and analysis on AI while allowing researchers, scientists, and the workforce for more productive and wholesome tasks. With Artificial Intelligence in Biotechnology, providers can eliminate manual functioning and focus more on innovation-driven tasks and enable AI for regular mundane requirements.
Increasing Quality of Agricultural Biotechnology: Biotechnology is crucial in modifying and transforming plants to grow better crops (quality and quantity). The agricultural biotechnology sector also uses it for harvesting, packaging, yielding, and other essential activities. AI is a game-changer in agricultural biotechnology because it helps plan the next yield by following a pattern designed to keep everything in check, like weather forecasts, data on farmland, insights on nature, and availability of raw materials like manure, pesticides, and seeds. AI-based tools are important for understanding the features of the crop, comparing different crop qualities, and predicting a plausible yield. Another important modern solution is RPA (Robotics Processing Automation), an extended arm of Artificial Intelligence. RPA helps in the analysis and automation of the farmlands.
Discovering New Drugs and Vaccines: In the past decade, the world has witnessed newer diseases, viruses, and flues spreading across continents like Covid-19. It has alarmed the biotechnology industry to stay ahead of time to discover and develop newer drugs and vaccinations that can combat such diseases. With the growing dependency on Artificial Intelligence and Machine Learning, the process of identifying the right molecules, synthesizing them in laboratories, analyzing the data for efficacy, and supplying it in the market.
The role of AI in the biotechnology industry relies on research and development as innovations and discoveries need detailed study.
The process is time-taking, expensive, and strenuous. Businesses choose Artificial Intelligence, Machine Learning, Big Data Analytics, and Neural Networks to make them more coherent and productive. The aim is to make human life more progressive on multiple fronts; therefore, the role of AI in the Biotechnology Industry, will greatly impact the world.
Now is the time for biotech companies to be more active and choose advanced AI technologies for their business to boost efficiency and results. Companies need to speed up their research and bring forth never-heard-before inventions to stay ahead of their competitors. Biotech firms are revamping their usual process by patterning with AI service providers with the help of Machine Learning (ML) and Artificial Intelligence (AI).
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Thomas John is the President & CEO of Calpion Inc. Thomas brings more than three decades of experience in the healthcare RCM & AI industry. He has expertise in strategizing enterprise-level IT solutions using AI & Deep Learning for various organizations, from Fortune 500 to high-scalability startups. Thomas completed the Information Technology in Healthcare leadership program at Harvard T.H. Chan School of Public Health. With a background and professional experience in advanced technologies, he supports our clients in their digital transformation journey for business success. Thomas believes in using the latest advanced technologies like AI to improve businesses for operational excellence.