Raqamli texnologiyalarning Yangi O‘zbekiston rivojiga ta’siri

 
RAQAMLI TEXNOLOGIYALARNING 
YANGI 
O‘ZBEKISTON
 RIVOJIGA 
TA’SIRI
 
Xalqaro ilmiy-amaliy konferensiyasi
 
The integration of AI in drug discovery and precision medicine offers several advantages for 
accelerating the development of new therapies. AI algorithms can analyze vast amounts of data, 
including genomic information, molecular structures, biological pathways, and clinical data, to 
identify potential drug candidates more efficiently. This computational power enables researchers 
to streamline the identification and prioritization of promising drug candidates, optimizing the 
early stages of drug discovery. 
AI can also facilitate the discovery of biomarkers, which are crucial for patient stratification, 
treatment response prediction, and identifying potential targets for therapeutic intervention. By 
analyzing complex datasets, AI algorithms can identify patterns and correlations that may be missed 
by conventional approaches, leading to the identification of new biomarkers. These biomarkers can 
aid in the development of personalized treatment approaches, advancing precision medicine. 
Moreover, AI has the potential to transform the process of drug design. By leveraging machine 
learning and computational modeling techniques, AI can predict the properties and behavior of 
drug candidates, allowing for more accurate predictions of efficacy, safety, and potential side effects. 
This computational screening helps researchers prioritize and optimize their experimental 
validation efforts, reducing the time and cost associated with unsuccessful drug candidates. 
Despite the immense potential of AI in drug discovery and precision medicine, challenges 
remain. One crucial challenge is the need for collaboration and close cooperation between medical 
professionals, computer scientists, and engineers to define the requirements for effective AI 
assistance. The interdisciplinary nature of AI-driven drug discovery and precision medicine 
highlights the importance of a team approach in harnessing the full potential of AI technologies. 
Nevertheless, successful applications of AI in drug discovery and precision medicine continue 
to emerge. AI has been instrumental in supporting clinical decision-making and has shown promise 
in risk stratification, genomics, imaging and diagnosis, precision medicine, and drug discovery. 
These advancements pave the way for a future where AI-driven approaches will contribute to faster, 
more efficient drug development and personalized treatments.