More promising targets for the design of new drugs are identified than ever before due to an explosion in knowledge about various diseases’ molecular mechanisms.

However, the processes involved in drug discovery can be complex. 

This blog lets us see how AI and machine learning in drug discovery can eliminate the traditional barriers, allowing us to get new and more effective drugs.

How Is AI Used In Drug Discovery?

The primary goal of drug discovery research is to identify medicines that have a beneficial effect on the body – in other words, medications that can help prevent or treat a specific disease.

Although there are many different types of drugs, many are small chemically synthesized molecules. These are capable of binding specifically a target molecule present in the disease.

Traditional drug discovery methods are target-driven, which means that a known target is used to screen for small molecules that interact with it or affect its function in cells.

These approaches work well for easily druggable targets with a well-defined structure and well-understood intracellular interactions. However, due to the complexity of cellular interactions and a lack of knowledge of intricate cellular pathways, these methods are minimal.

AI in drug discovery market can overcome these obstacles by detecting novel interactions and determining the functional significance of various cellular pathway components.

AI uses complex algorithms and machine learning to extract meaningful information from large datasets. For example, AI uses RNA sequencing data to identify genes whose expression correlates with a given cellular condition.

AI can also identify compounds that could bind to ‘undruggable targets' or proteins with undefined structures. A predictive set of compounds can be easily placed in a relatively short amount of time by iterative simulations of interactions of different compounds with small pieces of a protein.

Furthermore, even if a new drug candidate shows promise in laboratory testing, it may still fail in clinical trials. In fact, less than 10% of drug candidates make it to market after completing Phase I trials.

With that, it is not surprising that experts are now looking to AI systems' unparalleled data processing potential as a way to accelerate and lower the cost of discovering new drugs. AI has the potential to save more than US$70 billion in drug discovery costs by 2028, according to market research firm Bekryl.

The Advantages of Using AI in The Drug Discovery Market

The vast size of the libraries used to screen for new drug candidates. Still, it is now nearly impossible for individual researchers to review everything themselves - which is where AI and machine learning can significantly benefit.

The application of artificial intelligence to drug discovery can transform the current time scale and scope of drug discovery.

• For drug discovery, AI does not rely on predetermined targets. As a result, subjective bias and prior knowledge play no role in the drug development process.
• AI makes use of the most recent advances in biology and computing to create cutting-edge drug discovery algorithms. As a result, AI can level the playing field in drug development due to the rapid increase in processing power and reduced processing costs.
• AI has a higher predictive power when it comes to defining meaningful interactions in a drug screen. As a result, the possibility of false positives can be reduced by carefully designing the parameters of the assay in question.
• AI can move drug screening from the bench to a virtual lab, where you can obtain results faster and can shortlist promising targets without the need for extensive experimental input and staffing hours.

These are the potential benefits of the drug discovery pipeline. But with the evolution of drug discovery and the reduction of attrition rates, which will ultimately result in more novel drugs reaching patients faster.

But, for the time being, we are hampered by prohibitive costs. But, with time, competition emerges, prices will fall, opening up exciting opportunities for discoveries in various fields.

The Rise Of AI In Drug Discovery Market

The role of Artificial intelligence in drug discovery is expected to grow in the market from 2020 to 2027 due to an increase in the number of cross-industry collaborations, increase in venture capital investments, increase in R&D activities for the use of AI technology, and a rise in the importance of drug discovery.

According to Data Bridge Market Research, the market will be worth USD 3,932.87 million by 2027, growing at a CAGR of 40.5 percent during the forecast period. The growing awareness of the benefits of artificial intelligence among physicians and patients has directly impacted the market's growth. 

Factors that will drive the growth of AI in the drug discovery market:

• Increase need to reduce cost and time.
• Adoption of cloud-based services and applications.
• Expansion of pharmaceutical industries
• Delay in patent expiry.

The expansion will aid you in analyzing meager growth segments in the industries and provide users with a valuable market overview and market insights to assist them in making strategic decisions for identifying core market applications.

How AI And ML Is Used In Drug Discovery?

According to Taconic Biosciences, drug development takes an enormous amount of time and money. Bringing a drug to market costs approximately $2.8 billion.

AI and machine learning can be beneficial at all stages in the drug discovery process. For example, Healthcare AI startups raised more than $2 billion in 2020 using AI to streamline the drug manufacturing process, receiving some of the enormous sums compared to startups deploying the technology in other healthcare segments. The stages where AI employed in drug discovery are:

Phase I  - AI in Drug Discovery

The drug discovery process includes everything from reading and analyzing existing literature to testing how potential drugs interact with targets.

Phase II - AI in Preclinical Development

During the preclinical development stage of drug discovery, potential drug targets are tested on animal models. Using AI during this phase could help trials run more smoothly and allow researchers to predict how a drug will interact with the animal model more quickly and successfully. 

Phase III - AI in Clinical Trails

After passing the preclinical development phase and receiving FDA approval, researchers begin testing the drug on human subjects. Overall, this is a four-stage process usually regarded as the most time-consuming and expensive stage of the drug-making process.

AI can help with monitoring during clinical trials by generating a more extensive set of data faster, and it can also help with retention by personalizing the trial experience.

As a result, AI can quickly identify many compounds in a relatively short period and at a quarter of the cost of traditional methods.

Quytech

Quytech offers Artificial Intelligence products and services to the healthcare industry, including hospitals, data centers, research organizations, and drug discovery. We are currently working on an AI cancer prediction project. Our team specializes in developing and designing solutions that have a high success rate in cancer diagnosis. We can predict more stable and effective outcomes for cancer patients by using ML and neural networks generated data.