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| Main Authors: | , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Drug development research
2026
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/41630488/ |
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Table of Contents:
- Artificial Intelligence in Drug Discovery: Integrative Advances From Data to Therapeutic Innovation. Mehran, Mohammad Javad Mohammadzadeh, Sara Bolideei, Mansoor Barzigar, Rambod Haider, Khawaja Husnain Jadgal, Nasir Bahrami, Yadollah Drug Discovery Artificial Intelligence Humans Reinforcement Machine Learning Deep Learning Machine Learning Animals Drug Design Integrating artificial intelligence (AI) into drug discovery revolutionizes pharmaceutical research by significantly accelerating the identification, optimization, and development of novel therapeutics. Conventional drug discovery methods, known for high costs, lengthy timelines, and low success rates, are increasingly being augmented by AI-based technologies, including machine learning (ML), deep learning (DL), and reinforcement learning (RL). These advanced computational approaches enhance key processes, such as target identification, virtual screening, de novo drug design, toxicity prediction, and the optimization of pharmacokinetic and pharmacodynamic profiles, dramatically increasing overall efficiency. AI-driven primary and secondary screening methods improve cell classification, compound prioritization, and drug-target interaction predictions, substantially shortening the progression from preclinical phases to clinical trials. Additionally, AI enables retrosynthesis prediction and reaction yield modeling, optimizing chemical synthesis pathways and reducing the need for resource-intensive experimental procedures. AI's integration into clinical trials has notably improved patient stratification, biomarker discovery, and adaptive trial designs, ultimately delivering more precise and economically feasible therapeutic interventions. Furthermore, AI supports polypharmacological approaches through multitarget drug discovery, drug repurposing (finding new uses for existing drugs), and adverse effect prediction, thereby advancing personalized medicine. Despite these transformative advantages, it's important to note that AI in drug discovery also has limitations, such as ensuring data quality, improving model interpretability, gaining regulatory acceptance, and addressing ethical concerns. This review comprehensively explores the impact of AI throughout the drug discovery pipeline, emphasizing its critical role in expediting the development of life-saving medications and outlining future directions for continued pharmaceutical innovation driven by AI.