The Role of Artificial Intelligence in The Development of Vaccines and Drugs

The world of medicine is growing with each passing year. The researchers have proposed new technologies to help medical health experts with disease diagnosis and treatment. The modern age medical diagnostic system is highly dependent on machines, software, and expert systems. We will now review the role of artificial intelligence in vaccine development and drug discovery.

If we talk about the 1950s, hundreds to thousands of Americans were reported to experience great difficulty due to measles infection. However, in the year 2015, after the development and continuous use of vaccines, the reported cases are not more than 191.

Vaccines: an effective weapon against diseases

There is no doubt to say that vaccines are one of the most powerful weapons against infectious diseases. But the biggest trouble is that they take years to develop. When we are in the middle of the COVID-19 pandemic, we cannot wait so long for the arrival of a vaccine.

Therefore, it is important to look for some modern solutions to these modern age problems. One of the best recommendations from experts is to use machine learning and artificial intelligence for the development of vaccines. This sounds like a great idea; so, let us have a detailed discussion over it.

The concept behind vaccine development

When we talk about the design of a vaccine, there are basically a number of factors involved. It is important to mention that vaccines work by exposing your body to certain kinds of pathogens that can be helpful for your immune system to present a quick and robust response against a specific kind of disease in the future.

The year-old vaccines were mainly developed using dead viruses that were observed to be safer to use but many times, they were ineffective on viruses with higher safety risks.

How new vaccines function

The recently developed vaccines on the other side tend to contain a specific range of components of the viruses that are considered to be more effective and safer as well.

For instance, the vaccine for hepatitis B used to contain surface protein. However, future vaccines can have some specific viral protein fragments. No matter what kind of procedures are followed for the development of vaccines, the main goal is to include some viral components that are immunogenic in nature.

They are expected to be highly visible to the immune system and have the potential to boost its action. It could further help in the development of new drugs to treat the symptoms of the disease in the human body.

Using AI and Machine Learning for vaccine and drug development

Over the past few years, researchers working in the field of immunology, artificial intelligence, and machine learning have been studying several properties of viruses that can be used to make them immunogenic. One of the key properties in which specific parts of the virus can be targeted by the antibodies:

• Proteins that are produced by the B-cells can prevent viral entry to the cells while inhibiting the spread of the virus in the body.
• Another important property that demands the researcher’s attention is that which viral protein fragments can be presented on the surface of the human cell so that it can be marked infected and then killed by the T-cells.

Machine learning algorithms in healthcare

Some researchers have recently used machine learning algorithms to make predictions about the strength of properties for the viral fragments. These models can help to choose the most effective parts of the viruses that can show immunogenic behavior are must be included in the vaccine.

Artificial intelligence, machine learning, and deep learning models can recognize patterns from massive sets of training examples. Conducting such operations manually can be much time-consuming and complicated as well.

For instance, the immunologists have recently selected almost one million protein fragments which are required to be presented to the cell’s surface and are visible to the T-cells.

It is difficult to recognize those fragments from human eyes to pick the most effective one for the diagnosis of a specific disease. But the machine learning models can learn through patterns containing millions of samples and they build an automatic understanding of which properties or fragments could be most useful for a specific type of diagnosis.

Artificial Intelligence and Covid-19 vaccine

With its first identification in Wuhan, China in the month of December 2019, Covid-19 started spreading in the world late in the month of January.

The researchers have used several machine learning models to identify the immunogenic components of this dreadful virus so that they can be used to detect effective vaccine candidates.

The studies show that researchers have scanned almost every protein present on SARS-CoV-2 which is responsible for the Covid-19 virus with a goal to identify regions that have strong antibody targets. Some results show that SARS-CoV-2 spike protein is effective enough to be targeted by the antibodies and it plays an important role in the virus’s entry into the lung cells.

Covid-19 vaccine development

After obtaining several findings relevant to the Covid-19 virus using AI algorithms, many companies have now started the development of the Covid-19 vaccine. However, many other trials of this proposed vaccine are still pending that may take few more months to confirm the clinical efficacy of the vaccine.

There is no doubt to say that this is an early era for the training of machine learning or deep learning models for the design of vaccines. Although these algorithms are very good at handling training data, the limited or smaller dataset reduces the efficiency of design.

Studies reveal that artificial intelligence models work well when used on a massive amount of data as it helps to improve the reliability of the network. However, the process could be much improved in the near future.

How Artificial Intelligence speeds up Covid-19 vaccine development

Thanks to the ability of the advanced technologies that could help to run computer simulations relevant to the viruses. The fully efficient models can dramatically speed up the design process and it can naturally help to reduce fatalities worldwide.

It is exciting to hear that technologies are able to enhance the results of the medical diagnosis system that could prevent the world from the disastrous impacts of a pandemic.

Many researchers are also working on model customization to receive improved results for the training of vaccine-related models. These studies could further help in drug discovery to improve the diagnosis and treatment outcomes.

This will provide new options for both clinicians and medical companies to develop and optimize treatments for specific diseases.

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