Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! By
The Role of Artificial Intelligence in Pharmaceutical Sciences Publisher



M Sanaei MARZIEH ; Md Nedamani Mohammadebrahim DELZENDEH ; H Bakherad HAMID
Authors

Source: Journal of Isfahan Medical School Published:2025


Abstract

Background: Drug discovery and development affects human health and the drug market. However, investing in a new drug is often a complex and difficult challenge due to the long and complex drug research and development process. Methods: With the advancement of experimental technology and computer hardware, artificial intelligence has recently emerged as a tool for analyzing abundant and high-dimensional data. Explosive growth in the size of biological data provides advantages in applying artificial intelligence in all stages of pharmaceutical research and development. Findings: Similar to human learning models, machine learning and deep learning can gradually recognize different features of data, and update their model parameters through continuous iterations until a valid model is formed. Conclusion: This article begins with a brief overview of common AI models in drug discovery. Then, it briefly discusses their specific applications in different drug research and development phases, such as target discovery, drug discovery and design, preclinical research, and effects on the drug market. Finally, major limitations of artificial intelligence in pharmaceutical research and development are discussed. © 2025 Elsevier B.V., All rights reserved.
Related Docs
View other Related Docs
1. Machine Learning and Artificial Intelligence in Modern Medicine, (2024)
2. Drugtar Improves Druggability Prediction by Integrating Large Language Models and Gene Ontologies, Bioinformatics (2025)
3. Deep Neural Network in Qsar Studies Using Deep Belief Network, Applied Soft Computing Journal (2018)
Experts (# of related papers)
View all Related Experts
Fahimeh Ghasemi (8)
Afshin Fassihi (6)
Other Related Docs
4. Accelerating Big Data Analysis Through Lasso-Random Forest Algorithm in Qsar Studies, Bioinformatics (2022)
5. The Role of Different Sampling Methods in Improving Biological Activity Prediction Using Deep Belief Network, Journal of Computational Chemistry (2017)
6. The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review, Advanced Biomedical Research (2024)
7. A Gu‑Net‑Based Architecture Predicting Ligand–Protein‑Binding Atoms, Journal of Medical Signals and Sensors (2023)
8. Estimation of Betamethasone Release Profiles From an in Situ Forming System Based on the Biodegradable Polymer Using Artificial Neural Networks, IFMBE Proceedings (2009)
9. Heter-Lp: A Heterogeneous Label Propagation Algorithm and Its Application in Drug Repositioning, Journal of Biomedical Informatics (2017)
10. A Deep Learning Approach to Predict Inter-Omics Interactions in Multi-Layer Networks, BMC Bioinformatics (2022)
11. Application of Artificial Neural Networks in Controlled Drug Delivery Systems, Applied Artificial Intelligence (2010)
12. Prediction of Aptamer Affinity Using an Artificial Intelligence Approach, Journal of Materials Chemistry B (2024)
13. A Qsar Study of Some Cyclobutenediones As Ccr1 Antagonists by Artificial Neural Networks Based on Principal Component Analysis, DARU, Journal of Pharmaceutical Sciences (2011)
14. A Review of Network-Based Approaches to Drug Repositioning, Briefings in bioinformatics (2018)
15. Artificial Intelligence for Radiotherapy Dose Prediction: A Comprehensive Review; Utilisation De L'intelligence Artificielle Pour La Prediction De Dose En Radiotherapie : Revue De La Litterature, Cancer/Radiotherapie (2025)
16. A Deep Learning Model Based on the Bert Pre-Trained Model to Predict the Antiproliferative Activity of Anti-Cancer Chemical Compounds, SAR and QSAR in Environmental Research (2024)
17. Druggen Enhances Drug Discovery With Large Language Models and Reinforcement Learning, Scientific Reports (2025)
18. Deep Attention Network for Identifying Ligand-Protein Binding Sites, Journal of Computational Science (2024)
19. Generative Artificial Intelligence: In the Search for New Landscapes in Basic and Clinical Nephrology, Journal of Research in Medical Sciences (2025)
20. Identification of New Potential Candidates to Inhibit Egf Via Machine Learning Algorithm, European Journal of Pharmacology (2024)