AI in Biotechnology: Applications, Benefits, and Career Scope
Transforming modern science through computational power.
The advancement of Artificial Intelligence (AI) is extremely beneficial to scientists and researchers involved in biotechnology by allowing them to more quickly and accurately analyze large amounts of complex biological information, determine how diseases tend to develop based on historical data, search for new drugs and other treatments, and help improve different aspects of healthcare solutions. Traditionally, biotechnology study relies on experiments conducted in a laboratory, information obtained from biological databases, and lengthy laborious research; however, much of the process with the assistance of AI will be faster, more precise, and based on data rather than intuition or experience.
More and more PhD candidates, researchers, educators, and individuals currently employed in the biotechnology industry are becoming informed regarding the implementation of AI in biotechnology because it is no longer being utilized by those solely trained as computer programmers. Students learning life sciences, students studying biotechnology, bioinformatics researchers, and employees working for large pharmaceutical companies that research and develop medication can all benefit from gaining an understanding of how AI tools are currently being used in biotechnology.
What is AI in Biotechnology?
AI in biotechnology includes the application of data and computational models (machine learning, deep learning, and data analytics) to find solutions for problems faced in the areas of biology and biomedical science. Each of the technologies mentioned above is used to find biological trends from large amounts of data, including, but not limited to, DNA sequences, protein structure, clinical outcomes, molecular data, and imaging data.
To reduce the time and increase the accuracy of manually analyzing biological samples, researchers apply AI models and methods to detect patterns, classify using/testing methods for the purpose of making predictions, as well as use models/methods from AI to support the process of making data driven decisions.
Why AI Matters in Biotechnology
There is a huge amount of data produced every day through the use of modern biotechnology and includes a variety of different scientific disciplines such as genomics, proteomics, clinical trials, molecular screening, and biomedical imaging, all of which on their own generate multiple complex datasets. When using traditional analysis methods, it often takes a significant amount of time to analyze large quantities of biological data and there is frequently a lack of automatic ways to uncover hidden patterns and trends.
Artificial intelligence (AI) can help increase the speed of the biological data analysis, enhance the accuracy of predictions in predictive-based research studies, shorten the amount of time required for drug discovery, facilitate personalized medicine by enabling researchers to make data-driven decisions faster, and improve disease diagnosis and treatment plans.
Examples of the key uses of AI in biotechnology include the following:
1) Drug Discovery:
One of the primary uses of AI in biotechnology is in the field of drug discovery. Traditionally, the process of discovering and developing a new drug can take many years to complete and involve billions of dollars in investments. Various AI techniques can support researchers in screening very large libraries of chemical compounds, identifying potential lead compounds to develop, predicting how the lead compounds will behave in vitro and in vivo and decreasing the time spent conducting early-stage research.
AI algorithms can also help researchers make predictions regarding the potential toxicity of the lead compounds, the likelihood of the compounds exhibiting drug-like properties, and the chance of any of the lead compounds interacting with other chemical compounds or biological targets.
2) Genomics (DNA or Genetic Studies):
Another area of biotechnology that has relied heavily on the use of AI is genomics (the study of genes). AI can assist researchers in analyzing genetic data sets to identify mutations, assess risk factors related to mutations, and aid in the development of precision medicine. AI algorithms can be utilized to identify patterns within genomic sequences that would otherwise be challenging to find using more traditional methods.
AI algorithms are especially useful for researchers who are investigating cancer, inherited diseases and developing personalized health care systems for patients.
3: AI for Diagnosing Diseases
The application of Artificial Intelligence (AI) to disease detection and diagnosis is growing rapidly. Utilizing AI technology to process information such as medical images, blood tests, genetics and historical patient information can aid early detection of diseases.
As an illustration, AI may be used to find biomarkers that indicate the existence of diseases as well as classify disease stages.
4: AI for Predicting Protein Structures
Proteins are vital for biological processes to occur. It is critical to understand how proteins function in order to develop drugs, study diseases and carry out research in molecular biology. AI methods can help predict how proteins fold and interact with other molecules.
AI will enable us to take a more informed approach to structural biology and drug design using computational techniques.
5: AI for Individualized Healthcare
Individualized healthcare is the process of providing the best possible medical treatment to a patient by researching the patient’s genetics, lifestyle and health conditions. AI can analyze a variety of patient-specific data and provide researchers with information about which treatment will be most effective for a given patient.
Individualized healthcare will continue to evolve as a viable option in cancer treatment, rare disease study and precise medical care.
Important Skills to Learn
Those who are interested in pursuing careers that involve the application of artificial intelligence (AI) within the field of biotechnology should focus on these key skill areas:
- Fundamental knowledge of biology and biotechnology
- Introduction to AI
- Fundamentals of machine learning
- Programming in Python
- Analyzing biological data
- Utilizing bioinformatics tools and techniques
- Creating data visualizations to display data
- Understanding how to interpret scientific research and write quality scientific reports
Students just starting out with programming should take their time and follow a guided training approach to help facilitate incremental skill development through hands-on practice.
Career Scope of AI in Biotechnology
The various applications of AI within the field of biotechnology open up a wide variety of career opportunities, including:
Professionals who can combine knowledge of biotechnology with the skills necessary to apply AI to various aspects of biotechnological research and development will find opportunities for employment in research laboratories, pharmaceutical companies, biotechnology startup organizations, hospitals, academic institutions, and healthcare technology companies.
Why Students and Researchers Should Learn This Now
Biotechnology is increasingly becoming more reliant upon data, and researchers who have the ability to evaluate biological concepts and utilize AI-based tools will gain a significant advantage over their peers. While AI will not replace an individual’s biological expertise, it will provide research scientists with additional means to utilize their biological knowledge by enhancing their ability to analyze complex datasets.
For students and PhD-level scholars, learning about AI in biotechnology has been shown to improve research productivity, quality of publication, and increase career opportunities.
Conclusion
Artificial intelligence (AI) has made a difference in the way biological research is carried out in biotechnology. AI will be integrated into drug discovery, genomics, diagnostics, and personalized medicine, and it is expected to become an integral component of modern life science research. Individuals who develop AI-based biotechnology skills will have a competitive edge in future opportunities in research, health care, and the biotechnology industry.
Call to Action:
To gain hands-on, mentor-led training in AI and biotechnology, look for NanoSchool’s upcoming AI and biotechnology workshops and certification programs for students, doctoral students, researchers, academics and industry professionals.