Drug discovery either with or without AI is typically a lengthy, complicated process. Each stage in the process can take years. This will limit access to the drug in question until research is complete.
Now, however, advances in the use of artificial intelligence are showing great promise. It might reduce the amount of time necessary for proper clinical research. It may also lower drug costs for research institutions and consumers alike.
Traditional Methods in Drug Discovery With No AI
Traditionally, researchers had to rely on personal and recorded knowledge. They could exploit it in order to halt the progression of a particular disease. Many times, additional research would be necessary to expose these pathways before drug discovery could even commence.
Then, a variety of potential candidates were tested. They did it to determine which utilized the identified pathway to combat the disease most effectively. These multiple, large-scale studies were performed by hand and took many years of sampling and testing.
Current Situation of AI in Drug Discovery
Now, with access to large libraries of compound assays, scientists are able to test thousands of samples at once using high-throughput screening. This automated testing can screen tens of thousands of samples per day to identify compounds that “hit”.
Or this automated testing can screen billions of samples to find compounds that fit the desired activity necessary to interfere with disease progression. These hits can then undergo continued research. It will eliminate unnecessary testing and cutting thousands of wasted hours and dollars that they would have spent on compounds and that provide no return.
Tradition Is Enhanced With AI
Advances in AI have further reduced the time required to isolate compounds with potential for therapeutic use. By turning to a computerized method of study rather than a physical one, researchers can cut the time. This is the time necessary to isolate a compound with therapeutic properties to months instead of years. The primary use for AI in drug discovery involves 3D simulation at the molecular level.
Using powerful 3D imaging programs, researchers are able to use structure-based design. They do it to visualize how a particular molecule may interact with certain parts of the biological pathways of disease. For example, researchers may use AI to study exactly how a particular protein of interest binds to other molecules around it. Then they can use this information to determine which types of molecules could help or hamper that protein’s effect on a disease’s mechanisms within the body.
Opportunities of Machine Learning
Machine learning allows researchers to build algorithms in order to develop a digital model of the targeted molecules. Developing such a screening profile allows researchers to proceed with screening only the compounds that fit within the parameters of the profile.
This automated decision-making processes eliminate the uncertainties and errors sometimes caused by humans.
What Can We Expect?
Currently, the development of a new drug can take as many as a dozen years and cost millions or billions of dollars. Unfortunately, only one in every 5,000 of these potential new drugs actually makes it to market.
AI technology moves forward and high-throughput screening is utilized more frequently. So researchers hope to further reduce the timeframe required for drug discovery by performing numerous digital studies first.
Being able to do this before moving to physical studies means that more patients can access potentially life-saving medications much more quickly.
Hopes remain high that this reduction in physical procedures and laboratory time will reduce the overall cost of bringing a drug to market. This will result in cost savings to consumers and pharma companies alike.
Petr is a serial tech entrepreneur and the CEO of Apro Software, a machine learning company. Whenever he’s not blogging about technology for itechgyan.com or softwarebattle.com, Petr enjoys playing sports and going to the movies. He’s also deeply interested in mediation, Buddhism and biohacking.