Revolutionizing drug discovery: Integrating artificial intelligence with quantitative systems pharmacology
Open accessAbstract
Quantitative systems pharmacology (QSP) provides a mechanistic framework for integrating diverse biological, physiological, and pharmacological data to predict drug interactions and clinical outcomes. Recent advances in artificial intelligence (AI) might transform QSP by enhancing model generation, parameter estimation, and predictive capabilities. AI-driven databases and cloud-based platforms might support QSP model development and facilitate QSP as a service (QSPaaS). However, challenges such as computational complexity, high dimensionality, explainability, data integration, and regulatory acceptance persist. This review critically evaluates the integration of AI within QSP, highlighting novel methodologies like surrogate modeling, virtual patient generation, and digital twin technologies. It also discusses current limitations and outlines strategies for future integration to enhance precision medicine, regulatory acceptability, and mechanistic interpretability in drug discovery and development.
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As QSP increasingly integrates with machine learning, the bottleneck of manual parameter extraction becomes more acute.
“As QSP increasingly integrates with machine learning [6, 7, 8, 9, 10], the bottleneck of manual parameter extraction becomes more acute.”
From Structured Schemas for Provenance‐Rich, <scp>LLM</scp> ‐Assisted <scp>QSP</scp> Model Calibration (2026) · cites this paper as [8]
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- Authors
- Goryanin, I., Goryanin, I., & Demin, O.
- Affiliations
- University of Edinburgh (GB)
- Corresponding author
- Goryanin, Igor
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 30
- Issue
- 9
- Pages
- 104448
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W4413003586
- Cited by
- 21
- References
- 45
- Citations by year
- 2026: 18 · 2025: 3
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Discovery Today (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- quantitative systems pharmacology, artificial intelligence, drug discovery, drug interactions, surrogate models, digital twin, model parameter estimation, explainability, regulatory acceptance, precision medicine, data integration
- MeSH terms
- Drug Development, Animals, Artificial Intelligence, Humans, Systems Biology, Drug Discovery, Precision Medicine
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