In-Silico Medicine: How Artificial Intelligence (AI) is Accelerating the Discovery of Drugs
Today, technology is positively transforming several sectors, including the healthcare sector. For instance, over the last few decades, the research area of pharmacology has developed greatly. This development has been possible due to advanced technologies available to analyze and measure the biological subjects and samples.
In this article, we’ll discuss in-depth about in-silico medicine and its present market situations.
What is In-silico Medicine?
In-silico medicine refers to the top pioneering drug organization powered through a drug discovery engine that scans several data samples to get the specific biological characteristics of different diseases.
Once the identification process of special biological characteristics in the particular disease is identified, the next step is to find favorable treatment options and use the artificial intelligence technique known as generative adversarial networks (GANs) for creating molecules that can suit the diseases. Also, the failure rates in the targeted discovery of drugs are high, one of the reasons why scientists and researchers started using AI technology in the preparation of drugs. Also, AI can help in the creation of new drugs that can easily work on the targeted diseases and can help in making effective in-silico medicine.
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Why is In-Silico Drug Design Important?
It is highly important, as structures of protein targets are available in a much better way via nuclear magnetic resonance (NMR), bioinformatics, and crystallography methods. The demand for computational tools having the potential to identify and suggest required drug molecules is getting higher day by day.
Also, to fight several dangerous diseases like malaria, tuberculosis, AIDS, etc., in-silico drug discovery can be helpful. Developing any drug using the traditional methods requires cost and time. Using the in-silico drug discovery method, it can be done in a much faster way and at less expense.
Present Landscape of Drug Discovery
A deep, productive model like generative adversarial networks and variational autoencoders are considered as most promising methods for the making of novel molecules because of their results in the test, image captions, speech, and virtual synthesis of photographs.
Virtual innovation of optimal and new lead candidates needs performing and exploring several objective optimizations in wider chemical space, as the particular model requires to balance and assist between the crucial factors like stability, toxicity, and drug activity.
As per BIS Research healthcare experts, the in-silico drug discovery market is expected to expand from $2,129.8 million in 2020 to $6,515.3 million in 2031, with a CAGR of 10.52% during 2021–2031.
Artificial Technology Becoming Essential to Drug Discovery
Drug development and discovery are not getting done at a much higher speed due to the adoption of advanced computer technology. Across several academia and industries, artificial intelligence (AI) is mostly used. Machine learning (ML) is the main element of AI and has got its way in several areas, including analysis and creation of data. Algorithm methods like ML requires a lot of computational and mathematical theories.
Machine learning is evolving as a crucial technology for drug development and discovery. The use of AI is helping in the faster process of big data related to drugs and helping researchers in the development and discovery of drugs by passing valuable insights to them.
Here is an example to further understand how AI is helping in drug discovery. Exscientia is globally known as a drug discovery organization using artificial technology. The main focus of this company is using AI with real-life knowledge and researching new drugs in a faster manner.
In 2020, Exscientia and Sumitomo Dainippon company collaborated and developed a drug known as DSP-1181, using artificial intelligence. The first phase of this drug took place in Japan. It was announced by the company that the drug will achieve the research stage within 12 months. Meanwhile, the average time required by other organizations is 4.5 years.
The organizations used advanced AI technology with deep experience and knowledge in pharmacology and chemistry on drug discovery. Exscientia has become the first company to announce the human trials for AI-designed drug delivery in 2021.
Now, this organization is responsible for developing two AI-based drugs that have entered human trials. Using AI for drug discovery can easily help to get the result in less time with less investment. It can create a huge impact for drug companies and healthcare in developing drugs.
Market Insight of In-Silico Drug Discovery Market
Factors driving the market growth include the rising force of decreasing medical errors, advancement in the technological field, and increasing adoption of drug discovery methods based on cloud applications.
Several new drug elements have been created using computer technology methods successfully. As per region, currently, North America has the most shares, improving healthcare, rising per capita income, and improving reimbursement policies. However, Europe and Asia-Pacific regions are expected to rise at a much higher CAGR during 2021–2031.
Conclusion
The lesser use of modern technology results in making the drug discovery process costly and time-consuming. The adoption of AI technology can help to provide a solution and make the same process achievable in less time.
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