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AI in the Biotech Industry – Trends, Use Cases, and Digital Future  READ NOW

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Impact of AI in the Biotech Industry – Trends, Use Cases, and Digital Future


Thomas John

Thomas John

Artificial Intelligence in Biotechnology

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.

In this blog :

What is Biotechnology?

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.

How the Journey for AI-enable Biotechnology Started?

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):

  • In the early stages, AI features such as expert systems and rule-based approaches contributed to basic activities like protein folding and drug design.
  • Less computing power and complexity in data gave rise to the need for AI in Biotech.

The emergence of Machine Learning (the 1990s-2000s):

  • As computational power increased, Machine Learning gained popularity in the biotech industry.
  • Supervised learning algorithms, such as decision trees, to analyze biological data and predict the properties of molecules.
  • Machine learning helped in gene expression analysis, biomarker discovery, and drug-target interaction forecast.

Deep Learning Revolution (2010s-present):

  • Deep learning, a subset of machine learning, became popular in the biotech industry with the start of Graphics Processing Units (GPUs) and extensive datasets.
  • Deep Neural Networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), revolutionized the biotech industry.
  • Deep learning models helped with image analysis, genomics, proteomics, and drug discovery.
  • CNNs also support image-based diagnosis, automated microscopy, and histopathology analysis.
  • RNNs assist in sequence analysis tasks such as DNA sequence classification and protein structure prediction.
  • Generative models like Generative Adversarial Networks (GANs) help in drug discovery, molecular design, and de novo molecule synthesis.
  • Integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (Present and Future)
  • The biotech industry is increasingly adopting a combination of Artificial Intelligence (AI), Machine Learning, and Deep Learning techniques for various applications.
  • These technologies analyze massive amounts of omics data, including genomics, proteomics, metabolomics, and transcriptomics.
  • They examine discovered drugs and development, personalized medicine, disease diagnosis, patient stratification, and treatment optimization.
  • AI-based algorithms are integrated with high-throughput experimental techniques to accelerate data analysis and decision-making processes.
  • Reinforcement learning is also gaining attention for optimizing experimental designs and identifying optimal treatment strategies.

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.

Contribution of Artificial Intelligence in the Biotechnology Industry:

  • Improved Precision: For drug discovery and manufacturing, bio technicians require a great deal of precision which is usually costly and difficult. AI and machine learning solve such challenges and standardize important activities such as analyzing chemical protein components, performing clinical trials, and making decisions based on the precise and trusted conclusions drawn. These latest technologies support biotech organizations in reducing the scope of error to produce quality results in a much shorter period.
  • Drug Discovery: With the help of AI, Machine Learning, and Deep Learning, Biotech companies collect volumes of data throughout the year and strategize how to use the stored data for different medical activities. They use data analysis to offer additional value to users through new drug discoveries. With the help of intelligent data analysis tools, biotech companies now understand how to synthesize drugs. These synthesized drugs help develop new treatments for all kinds of medical conditions. The involvement of data analysis for drug development helps in discovering new chemical substances to treat diseases. It also contributes to clinical trials, assisting drugs to reach the market faster without losing credibility.
  • Personalized Medicine: A new landmark was established in the drug development process when AlphaFold discovered how proteins unfold. Artificial Intelligence played a major role in establishing a modern ecosystem for personalized medicines and life sciences. AI-enabled software can help understand how diseases develop in human bodies and how special medication can cure them.
  • Gene Editing: The technology of Artificial Intelligence in biotechnology is refining to develop better medical solutions using big data to understand the human genome more efficiently. It will gradually lead to gene editing and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat). With the help of modern methods, personalized medicine is coming one step closer to reality. In the future, it will be probable to identify and diagnose certain diseases encoded in our genes before they grow. Patients can find out and treat heritable diseases before they occur.
  • Animal Biotech: Artificial Intelligence (AI) can help with animal biotech by using molecular biology to modify genes and traits of animals to create crossbreed versions for agricultural and pharmacological applications. Artificial Intelligence in biotechnology also helps in selective breeding, i.e., animals of a specific type are bred together to give birth to offspring of similar attributes. Machine Learning can also process big databases with genomic data about different animal types and methods to choose animals for selective breeding. With such calculative practices, animal breeding becomes a sensibly performed procedure for healthy, physically strong offspring to withstand diseases.
  • Industrial Biotech: Artificial Intelligence in biotechnology is the backbone of many activities, such as biopolymer replacement, molecular designing, and robotic processes. Artificial Intelligence and Deep Learning are a core part of industries as it helps with end-to-end operations. For instance, AI tools create a 3D image of molecules and edit the structure to build new chemical compositions. It becomes easier for laboratories to develop advanced chemicals for industrial use. AI also helps streamline workflow for textiles, fuels, car components, chemicals, and manufacturing industries. In the future, automated robots will replace humans for hazardous jobs to minimize life risks.
  • Agricultural biotechnology: Biotech companies are now using Artificial Intelligence and Machine Learning to develop autonomous robots that will perform agricultural activities such as harvesting crops faster than the human workforce. With the help of Deep Learning algorithms, crop fields will be under analysis, and drones will capture the data to monitor the health of crops and soil. The software solutions have Machine Learning algorithms that will track and predict numerous environmental and climatic changes like weather conditions, the temperature of the day during different hours, and more. With such digital dependency, we can see a rise in "smart agriculture." It brings "Agricultural Data Space" into existence as it adds great value to "Cognitive Agriculture" (COGNAC).
    In agricultural biotechnology, a balanced nutrient cycle is crucial for better productivity and sustainability of the production of crop and livestock products. Numerous suppliers with sensor systems record soil, plant, and weather data so farmers can obtain information about the nutrient balance to identify possible challenge areas. With an AI-enabled solution, "phenotyping" can be done as it studies plant science.
  • Forest Biotechnology: Forests are an important resource for our ecosystem and humanity. However, gradually due to deforestation, there is a loss of forest resources. Organizations are trying to increase forest sustainability by combining biotechnology with AI. It helps in improving forest plantation, which is very crucial for sustainable global demand.
    There are many benefits where AI-enabled solutions are present in the forest biotechnology industry, such as:
    • Predictive Modelling: Artificial Intelligence in biotechnology helps in analyzing data from satellite imagery, drone imagery, and other ways to estimate the growth of various species of trees in different areas. AI helps in optimizing the plantation and management of forests to enhance growth.
    • Pest Management: AI helps detect any spread of diseases or pests in the forests that are likely to impact the health and life of trees. It also supports identifying areas that are at risk and adopts preventive measures to safeguard forests.
    • Resource Management: Intelligence-enabled tools also help optimize the use of resources like water and nutrients in forests to minimize waste.
    • Inventory Management: AI helps to manage forests for different purposes like timber production, recreation, and conservation. The AI helps with data analysis of the age, location, type, and species of trees and resources to manage the demand for different products and services.
    • Monitoring Forests: AI helps to monitor the data from sensors to track the health of forests and potential risks like wildfire. It helps in keeping the forest safe and eliminates the scope of harm.
  • Bioinformatics: Machine Learning is already an established and prominently used solution in medical research for system biology; however, there are a few challenges in environmental science, like soil metaproteomics. Using high-throughput AI helps control environmental systems and discovers methods for sustaining ecosystems for human life on land and animal life in the forest. The deep learning algorithm is used for resource management as it supports predicting loopholes in large datasets. For example, crops and soil communicate as microbial and aim to increase volumes of sequencing data of microbial products. These products help in improving the nutrient value of soil and plant immunity, such as bio-stimulants, biofertilizers, biopesticides, etc
Subsets of AI in Biotechnology
  • Machine Learning and Deep Learning for Data Analysis
  • Genome Sequencing for Bioinformatics
  • Global Database for Scientists
  • Faster and Concise Insights from Humongous Life Sciences Data
AI in Biotechnology different fields
  • Industrial Biotechnology
  • Medical Biotechnology
  • Agricultural Biotechnology
  • Food Process

Top Artificial Intelligence Trends That Will Transform the Biotechnology Industry

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.

AI in Biotechnology market AI in Biotechnology market

Some of the functionalities of Artificial Intelligence in the biotechnology industry are as follows:

  • Drugs and vaccine innovation
  • Enhanced analysis of biotech data
  • Precise and expedited plant genome studies
  • Global connection of the biotech industry

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.

Future of AI in Biotechnology Future of AI in Biotechnology

  • Expanding Global Connections for Biotechnological Developments: Artificial Intelligence in Biotechnology is impactful in connecting scientists across the globe to have access to crucial data. Scientists and researchers connect to find newer medicines and industrial developments. Machine Learning algorithms have helped scientists to decode data and recognize the pattern of certain diseases in a distant country and use that analytical model to safeguard their geographic location. These AI-enabled scientific models have advanced with time and offer accurate medical information. We already see a growing inclination towards AI and related tools transforming the biotechnology industry in multiple ways. In the future, we will also witness that AI in Biotech industries will be used for the welfare of humankind by the progress it shows in the fields of biosciences. The potential of Artificial Intelligence in Biotechnology is such that it transforms challenges into opportunities. For instance, AI in Biotech industries supports collaboration and improves the visibility of your business to achieve regulatory compliance, track, and control inventory, maintain accuracy, and aims to integrate the industry's best practices for preventive and disciplinary actions.
  • Artificial Intelligence in Real-World Use Case: Artificial Intelligence and Machine Learning offer many clinical diagnostics and medical trial opportunities. For instance, Eyenuk is a company that develops Artificial Intelligence for medical technology and has created an EyeArt platform to detect diseases from retinal images. EyeArt can autonomously detect a patient's retinal images to identify signs of diseases and deliver an end-to-end report in just 60 seconds. The company hosted a clinical trial for 942 patients across 15 medical centers around the U.S. It found that about 95% of patients were sensitive to diabetic retinopathy. EyeArt used AI and Machine Learning to train algorithms on about 2 million images to ensure the maximum accuracy of its reports.
  • Biotech Software: Artificial Intelligence is gradually becoming a one-stop solution to some of the toughest situations on the industrial level. As the software evolves and modifies, we are closer to more ease and high-quality operations. However, like all other software solutions, Artificial Intelligence requires a deep understanding of the problem it must solve. In such situations, developers build commands that AI in Biotech companies can understand and act upon. Biotech requires specialized developmental knowledge to create a trusted and convenient digital product. If there is a lack of knowledge, it will result in low-quality products that will not contribute to the big cause.


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).

Are you looking for an AI solution to resolve your biotech challenges?

Contact us to know how our customized AI solutions have helped our biotech clients to improve their productivity and contributed to the discovery and development of drugs and clinical trials.

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Thomas John

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.

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