The future of pharmaceuticals: Artificial intelligence in drug discovery and development

Chen Fu; Qiuchen Chen

doi:10.1016/j.jpha.2025.101248

Volume 15 Issue 8

Sep. 2025

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Chen Fu, Qiuchen Chen. The future of pharmaceuticals: Artificial intelligence in drug discovery and development[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101248. doi: 10.1016/j.jpha.2025.101248

Citation:

Chen Fu, Qiuchen Chen. The future of pharmaceuticals: Artificial intelligence in drug discovery and development[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101248. doi: 10.1016/j.jpha.2025.101248

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The future of pharmaceuticals: Artificial intelligence in drug discovery and development

doi: 10.1016/j.jpha.2025.101248

Chen Fu^a,b,
Qiuchen Chen^{a,c
,
,}

a. Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, 110122, China;
b. Pharmaceutical Sciences Laboratory Center, School of Pharmacy, China Medical University, Shenyang, 110122, China;
c. Liaoning Key Laboratory of Molecular Targeted Anti-tumor Drug Development and Evaluation, China Medical University, Shenyang, 110122, China

Funds:

This study was supported by the National Natural Science Foundation of China (Grant No.: 82304564) and the Liaoning Province Education Department Scientific Research Funding Project (Grant No.: LJKZ0777).

Received Date: Jul. 30, 2024
Accepted Date: Feb. 21, 2025
Rev Recd Date: Feb. 13, 2025
Publish Date: Feb. 26, 2025

Abstract

Abstract

Artificial intelligence (AI) is revolutionizing traditional drug discovery and development models by seamlessly integrating data, computational power, and algorithms. This synergy enhances the efficiency, accuracy, and success rates of drug research, shortens development timelines, and reduces costs. Coupled with machine learning (ML) and deep learning (DL), AI has demonstrated significant advancements across various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials. Through molecular generation techniques, AI facilitates the creation of novel drug molecules, predicting their properties and activities, while virtual screening (VS) optimizes drug candidates. Additionally, AI enhances clinical trial efficiency by predicting outcomes, designing trials, and enabling drug repositioning. However, AI's application in drug development faces challenges, including the need for robust data-sharing mechanisms and the establishment of more comprehensive intellectual property protections for algorithms. AI-driven pharmaceutical companies must also integrate biological sciences and algorithms effectively, ensuring the successful fusion of wet and dry laboratory experiments. Despite these challenges, the potential of AI in drug development remains undeniable. As AI technology evolves and these barriers are addressed, AI-driven therapeutics are poised for a broader and more impactful future in the pharmaceutical industry.
- AI,
- Drugs,
- Research and development,
- Machine learning

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References(238)

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