Application of Artificial Intelligence (AI) in Drug Development

Artificial intelligence (AI) is a technology-based system that includes various advanced networks and tools. These tools can obtain information, formulate rules for using information, draw approximate or definite conclusions, and self-correct. With the rapid development of computer computing power and the advent of the era of big data, AI has ushered in great development opportunities for drug research and development. AI can complete work quickly, greatly saving drug development time and high research and development costs.

Traditional drug discovery is mainly divided into four complex processes: target selection and confirmation, lead compound discovery and optimization, preclinical research and clinical research. In the past, these steps required a lot of time and money, and the success rate was not high. However, these works have accumulated a large amount of experimental data, which also provides a basis for the application of artificial intelligence technology. Artificial intelligence technology aims to effectively analyze large amounts of data and plan and propose better solutions based on the learned data. Artificial intelligence can minimize the errors and interventions that humans may encounter in experiments, accelerate the entire process of drug development, and save a lot of costs.

Target recognition

Targets are the basis of new drug development, so the identification of drug targets is particularly important in the process of drug discovery. AI can search for potential target information from massive literature, biochemical attributes and genetic data sets, and automatically predict suitable targets (usually proteins). If the drug target can be predicted by computer in the early stage and the cycle of target discovery can be shortened, it is of great significance to drug development.

Drug screening and drug design

Drug screening and structural optimization need to target the target, find and evaluate the interaction ability of different candidate molecules with the target, and screen out and optimize suitable molecules. AI uses big data and machine learning methods to comprehensively use various existing information to predict the suitability of molecules to screen out compounds that can interact with the identified target molecules in an ideal way.

• Active compound screening

Drugs can act on multiple targets simultaneously in the human body, but if they act on non-targeted receptors, they will cause side effects. Artificial intelligence can screen candidate compounds and quickly screen out compounds that act on specific targets and have higher activity.

• Compound properties prediction

Unsatisfactory pharmacokinetic properties are the main reason for the failure of drug development in the clinical research phase. Therefore, the evaluation of the compound's druggability and safety in the early stage of drug development is of great significance for improving the success rate of drug development and reducing the cost of research and development. Different artificial intelligence tools can be used to predict various properties of molecules such as toxicity, activity, and solubility.

• Protein structure and protein-ligand interaction prediction

Understanding the structure and properties of proteins is extremely important in the initial stage of drug development. In computer-aided drug design, drug design based on receptor structure also plays an important role. Among them, molecular docking technology that simulates protein receptor interaction is widely used. Artificial intelligence can help structure-based drug discovery by predicting protein structure and ligand-protein interactions, ensuring better therapeutic effects.

Compound synthesis

In some cases, synthetic challenges limit the space available for design molecules. Therefore, organic synthesis is a critical stage of drug discovery. The traditional method is the reverse synthesis method. The first step of the reverse synthesis method is to recursively analyze the target compound and then sequentially convert it into smaller fragments or building blocks that are easy to buy or prepare. The second step is to confirm the reaction that converts these fragments into the target compound. The second step is the most challenging one because it is difficult for the human brain to find a large number of organic reactions in the literature to choose the most reasonable reaction. AI can very quickly predict synthetic routes comparable to medicinal chemists.

Clinical Trials

This part of the drug development process may be the slowest part. It takes a lot of time to manually analyze and monitor the data. Therefore, human error will also bring danger, which will eventually lead to failure of drug trials. Artificial intelligence algorithms have the ability to process huge data blocks, can automatically identify suitable candidates, improve patient compliance, reduce trial costs and improve the efficiency of identifying drugs and treatment effects.

Disease diagnostic biomarkers

Biomarkers are molecules found in body fluids (such as blood), which provide an absolute basis for determining whether a patient has a disease. Biomarkers are used to accurately locate the progression of the disease, making the process of diagnosing the disease safe and inexpensive. Artificial intelligence technology can work automatically and accelerate the discovery process of biomarkers.

Drug repurposing

Reusing drugs provides a cost-effective strategy for reusing approved drugs for new medical indications. The artificial intelligence system based on big data resources can scan a large number of clinical data resources of existing drugs to find new drug candidates and biomarkers that can predict diseases, further accelerating and reducing the risk of the drug development process.

Advances in artificial intelligence are constantly working to reduce the challenges faced by pharmaceutical companies, affecting the drug development process and the entire life cycle of products.