The drug development process is notoriously expensive. A large amount of analytical processes of drug preparation can be optimized using machine learning. In this way, years of work and millions of dollars of investment can be simulated.

Making medicine with artificial intelligence and its steps

Pharmaceuticals has four main stages that artificial intelligence has been able to enter in all of these stages:

• First step: Determining the goals for the intervention
• Second step: discovery of candidate drugs
• The third step: speeding up clinical trials
• Step 4: Finding biomarkers for drug detection

Drug development with artificial intelligence: setting targets for intervention

The first step in drug development is to identify the biological origin of the disease and its resistance mechanisms. Then, good targets for disease treatment should be identified, which are usually proteins. There are many techniques such as  short hairpin RNA screening  and  deep sequencing  . These techniques collect a large amount of data to identify possible target routes. However, the old methods to calculate the large volume and diverse data sources and find the related path are facing many challenges. AI algorithms can more easily analyze all available data and can even learn to automatically identify good protein targets.

Drug development with artificial intelligence: discovery of candidate drugs

In the next step, we need to find a compound that can react with the specified target molecules in the desired pathway. This step involves testing thousands or millions of possible combinations of compounds. In these experiments, the effects of these compounds on the target tissue and also their side effects such as toxicity should be investigated. These compounds can be natural, synthetic, or bioengineered (growing tissues that are made with the help of biomaterial engineers). However, the current mechanism is often imprecise and produces erroneous results. This process takes a long time to narrow down the options and arrive at the best drug candidates (known as drug leads). Artificial intelligence and machine learning also work at this stage. These algorithms can learn to identify the degree to which a molecule matches. They do this identification based on structural clues and molecular identifiers. They then select the best options with the fewest side effects from millions of possible molecules. This method saves a lot of time in drug design.

Artificial Intelligence Drug Development: Accelerating Clinical Trials

Finding suitable subjects for clinical trials is difficult. If the wrong people are selected, the testing process will be delayed and waste a lot of time and resources. Machine learning can speed up the process of designing clinical trials by automatically identifying suitable candidates. Also, machine learning ensures the correct distribution of groups participating in the experiment. Algorithms can separate good candidates from bad candidates. In addition, these algorithms can be used as a warning system for failed clinical trials. In this case, researchers are allowed to intervene and prevent the preparation of medicine.

Artificial Intelligence Drug Development: Finding Biomarkers for Drug Discovery

You can only treat the disease if you are sure of your diagnosis. Some methods are very expensive and require advanced laboratories and specialized people, such as genetic sequence screening. Biomarkers are molecules found in body fluids (usually human blood). These indicators can determine with a high percentage of certainty whether a person is sick or not. In this way, the disease diagnosis process will be safe and cheap. These indicators are also used to determine the location of the disease. In this case, doctors can more easily choose the right treatment and check that the desired drug works properly; But it is difficult to discover a biomarker for any specific disease and it takes a lot of time and money. For this reason, we are again seeing the use of artificial intelligence in medicine.

Artificial intelligence can speed up a high percentage of human tasks. These algorithms categorize molecules into good and bad candidates. This allows professionals to focus on analyzing the best results. Biomarkers are used to detect the following:

• Occurrence of disease in the shortest possible time - diagnostic biomarker
• The risk of disease occurrence in an individual - a biomarker of risk
• Probability of disease spread - Probability of disease progression - Biomarker of prevention
• Probability of a patient responding to a drug - a predictive biomarker

Artificial intelligence has countless applications in the field of medicine and treatment. The meaning of these countless users is that from recognizing the connection between genetic codes to the use of artificial intelligence robots for difficult surgeries, everyone is among the users of artificial intelligence in the medical field. Artificial intelligence with all its uses has been able to create a modern era in treatment and health services and take it to another level.