Akhmadjonova Gulhayo Rafiqjon qizi
Fergana Public Health Medical Institute
Assistant at the Department of Medical and Biological Chemistry
Majidova Merojxon Ahliddin qizi
Student of Fergana Public Health Medical Institute
Tel: +998911126377
Abstract
This article discusses the application of artificial intelligence technologies in the fields of medicine and biological sciences, particularly in pharmaceutics. The processes of drug discovery, testing, and production are complex, long-term, and costly. In organizing these processes efficiently, artificial intelligence emerges as an important tool. The article highlights how artificial intelligence contributes to accelerating drug development, reducing costs, and improving efficiency and safety.
Key words: artificial intelligence, pharmaceutics, drug design, molecule discovery, virtual screening, de novo design, pharmacokinetics, pharmacodynamics, clinical trials.
Introduction
In the 21st century, the rapid development of science and technology has had a significant impact on medical and biological sciences. Traditional drug development processes take 10–15 years and require substantial financial resources. Therefore, the pharmaceutical industry is increasingly using artificial intelligence to reduce time and costs while improving the effectiveness and safety of medicines.
1. Main Directions of Artificial Intelligence Application in Pharmaceutics
a) Drug design and molecule discovery
Artificial intelligence algorithms help identify promising candidates among millions of molecules, facilitating the creation of new drugs. Systems such as AlphaFold predict protein structures and are used to identify new therapeutic targets.
b) Virtual screening and de novo design
Traditional screening processes are time-consuming, whereas artificial intelligence enables rapid identification of potential molecules.
c) Clinical trials
Artificial intelligence is effectively used in clinical trials for patient selection, outcome prediction, and the creation of “virtual control groups,” making the process faster and more ethically acceptable.
d) Safety and toxicity prediction
Using artificial intelligence models, the potential toxic effects of molecules can be predicted in advance. This allows researchers to identify cellular toxic substances early and develop methods to prevent adverse effects.
e) Personalized medicine
Based on patients’ genetic profiles, artificial intelligence helps determine individualized drug dosages and treatment plans. This approach is widely applied in oncology and cardiology and contributes to reducing animal testing.
Main Applications of Artificial Intelligence in Pharmaceutics
Artificial intelligence is widely applied in pharmaceutical research, drug development, clinical studies, and personalized treatment approaches.
1. Drug design and molecule discovery
Artificial intelligence enables the prediction of new molecules, that is, forecasting their biological activity. Structure-based design involves creating molecules that match the structure of a specific target protein.
2. Prediction of drug–target interactions
Artificial intelligence calculates the binding strength between molecules and proteins and reduces the probability of off-target (adverse) effects.
3. Pharmacokinetics and pharmacodynamics modeling
ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) parameters are analyzed using artificial intelligence. New molecules are predicted based on their biological activity, and structure-based designs are generated to match target protein structures using artificial intelligence algorithms.
4. Optimization of clinical research
By analyzing patient data, artificial intelligence selects the most suitable groups for clinical trials, offering a cost-effective and efficient approach.
5. Personalized treatment
Based on genomic and proteomic data, artificial intelligence helps select patient-specific therapies and supports pharmacogenomic research.
Drug Design and Molecule Discovery Process
The drug development process is very long (10–15 years) and expensive (1–2 billion USD). Artificial intelligence significantly accelerates this process. The main stages include:
Target protein selection
Artificial intelligence algorithms identify disease-related proteins using genomic data.
Molecule generation
Artificial intelligence models, particularly generative neural networks (GANs, VAEs, Transformers), generate new molecular structures. For example, DeepMind’s AlphaFold achieved major success in protein structure prediction.
Molecule screening
Traditional experimental screening requires testing millions of molecules.
Drug–protein binding calculation
Using molecular docking and artificial intelligence algorithms, the energy states of ligand–protein complexes are predicted.
Majidova Merojxon Ahliddin qizi
was born on September 12, 2005, in Quva district, Fergana region.
She graduated from Secondary School No. 2 in her district. Currently, she is a 2nd-year student in the Pharmacy program of the Pediatrics Faculty at the Fergana Public Health Medical Institute.
To date, her article entitled “Water Resources Polluted by Industrial Waste” has been published in the ONVS Journal, and her article “Artificial Intelligence in Medical and Biological Sciences: New Opportunities in Pharmaceutics” has been published in the ActaCAMU proceedings.
