AI In Biotech: Innovating The Next Frontier Of Healthcare With Care

Genetic research and Biotech science Concept with test tube

Healthcare is evolving rapidly and becoming more complex and AI in Biotech is contributing to this evolution.

As the reaction to the COVID-19 pandemic has illustrated, analysis of infection data and the rapid development of vaccines has been enhanced by the use of Artificial Intelligence (AI). As the software analyses data, it identifies patterns and aids both diagnosis and predictive modeling of trends. 

Artificial Intelligence has made a significant impact in the healthcare sector. It provides a faster diagnosis of diseases and the clinical effectiveness of new drugs and helps to direct attention to new areas of research. Target market and supply chain analysis aids product distribution.

Successful implementation of intelligent algorithms, combined with the ever-increasing power and speed of computers, bodes well for AI’s evolution in all healthcare spheres. Accurate diagnoses of diseases and the design of optimal medical treatment become less of a dream and more of a future reality.

Is Biotech The Same As Bioscience?

Biotech (biotechnology) is a bioscience field in which technology is used to attempt to solve medical problems. Bioscience encompasses all the sciences which study life and biological organisms, whereas biotech applies additional technologies to research and develop new commercial products.

Medicine doctor touching an electronic medical record on virtual screen

Human beings have used the principles of biotech for thousands of years. Early farmers soon learned how to improve crop yields by choosing the best seeds to plant and how selective breeding of farm animals developed better stock. But those successful methods tended to be rather hit or miss.

During the 19th century, following the discovery of the existence of microorganisms, scientists like Pasteur and Lister laid the foundations for the development of antiseptics and vaccines. Fleming discovered penicillin during the early 20th century, and more recent work has produced drugs and vaccines to treat diseases like mumps, measles, diabetes, cancer, and hepatitis.

Further background to biotech and biotech investments is available here.

These advances were made possible by applying new technologies and experimental techniques. Today AI in the biotech sector promises to enhance and accelerate research and development and bring new products to the market.

What Impact Has AI Already Had On Healthcare?

Several organizations are already making successful use of AI in research leading directly to the commercialization of derived products.

AI Research Into Molecular Structures

Biotech company Atomwise has applied a convolutional neural network (CNN), used in everyday applications like Facebook’s image tagging, and developed algorithms that identified 12 billion theoretical small-molecule compounds. Vendors can use that information to synthesize interesting candidates and test them in a matter of weeks.

automation switch -High technology and genetic research

Meanwhile, Deep Mind, an AI research subsidiary of Google’s parent company Alphabet, recently released a ground-breaking map of human proteins.

This was achieved using AI software AlphaFold which can predict a protein’s structure in a fraction of the time previously devoted to expensive experiments. Deep Mind has made the information available in the public domain.

Understanding the structure of these proteins helps clarify their function and enables researchers to formulate treatments for diseases. Successful use of these predictions, for example, contributed to the development of current COVID-19 vaccines. Attention is now being directed to using these predictions to tackle neglected diseases like sleeping sickness.

AI In Clinical Diagnostics

Eyenuk, a company specializing in artificial intelligence eye screening, has developed a product called EyeArt, which the UK’s National Health Service (NHS) demonstrated has more than 99.6% sensitivity for detecting diseases from retinal screening. Pilot assessments have been completed, and EyeArt is being integrated into routine screening programs, particularly for diabetic patients.

Cancer treatment, as implemented by Concert HealthAI, uses AI to guide how patients should be treated and to analyze their response and progress. With information on tumor status and other features, the AI model is then used to design a program for further personalized treatment. All of this is accomplished in real-time instead of the usual weeks of data preparation.

The Future Of AI In Biotech on Healthcare

AI in Biotech DNA strand analysis

The large volumes of data assembled by AI algorithms appear daunting, but AI excels at processing large datasets, predictive analysis, and recognizing patterns. Virtual trials of the effectiveness of new medications can eliminate the expensive and time-consuming human trials of the past and provide information before the drug is even tested on human beings.

IN Carta, cell image analysis software developed by Cytiva automatically classifies cells by phenotype and provides 3D 360-degree image analysis. The software generates 3D models of in vivo internal organs and other tissues, allowing visual analysis and enhancing diagnosis and possible therapies. 

Other companies, like BioXcel Therapeutics, use AI to review millions of publications to identify new uses of known drug classes allowing existing, well-known drugs to be used for treatments no one had considered before. They and other biotech companies are incorporating AI into their manufacturing processes, and others are using high-speed imaging to detect and reject defective drug capsules.

Researchers from the MIT – Massachusetts Institute of Technology have used AI to identify a new structurally different antibiotic candidate, which has shown to be effective against some bacteria resistant to all currently known antibiotics. The problem of increasing numbers of pathogens resistant to existing antibiotics prompted using predictive computer models in the search for new solutions.

The MIT software was designed to screen millions of molecules in a matter of days and to identify potential antibiotics using different mechanisms to kill bacteria. Several others dissimilar to existing antibiotics have been identified and are earmarked for possible development and marketing.

Best of all, AI control in manufacturing can increase productivity when intelligent algorithms refine and optimize process control.

What About The Commercial Aspects Of These New Products?

Development of nuclear or atomic technology

Deep Mind’s decision to make their map of human proteins available in the public domain is admirable, but much more work is required if scientists are to translate that information into drug design and viable medications.

Off-the-shelf or open-source software may allow for the rapid processing of the reams of data now available, but algorithms developed with specific commercial development in mind are proprietary and potentially extremely valuable. Open-source software may have been customized with specific biases or coefficients. The data used may also have been represented uniquely.

The final software product may also be incorporated into some medical devices, thereby enhancing its effectiveness for diagnosis or the development of new drugs or therapies. The presentation of the results of the processed data and the look and feel of the user interface may increase its market value.

Advances in diagnostics, pharmacology, and drug design benefit enormously from AI, and companies that have invested in their developments would be wise to patent their technology.


Productive AI in biotech is a reality, and it is set to increase its influence. With the increasing computerization of health services, ever-increasing volumes of data are being generated. Suitably designed AI algorithms can extract relevant datasets rapidly and analyze the output as required. 

AI is additionally able to provide a real-time and accurate diagnosis of a range of diseases in a way previously considered unimaginable. Virtual testing of speculative molecules leads the way to the design and potential marketing of new and more effective drugs. 

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