Artificial intelligence (AI) as a catalyst for mechanistic target discovery: Integrating systems pharmacology and multimodal data.

The AI Revolution in Drug Discovery: From Computational Innovation to Clinical Translation

The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.

Artificial Intelligence–Driven Integration of Metabolic and Genetic Biomarkers in Precision Oncology

Conventional pharmacogenomic markers, including polymorphisms in CYP or DPYD genes, often fail to accurately predict drug metabolism within solid tumors. This discrepancy occurs because drug metabolism is an adaptive phenotype influenced by the tumor microenvironment and host factors rather than a static inherited trait. Therefore, a transition from variant-focused models to a dynamic systems pharmacology framework using artificial intelligence (AI) is proposed in this review. This review evaluates AI architectures, including graph convolutional networks (GCNs), transformers, and reinforcement learning, for their ability to synthesize highdimensional data. These models process multi-omic and spatially resolved metabolomic data to track the shifting nature of tumor biology. We emphasize explainable AI (XAI) for causal consistency and federated learning for privacy-preserving, multi-institutional collaboration. AI models can effectively reverse-engineer metabolic states, forecast therapy resistance, and map evolutionary pathways in tumors. By integrating diverse data streams, these systems reveal the regulatory networks governing individual patient responses. The findings emphasize that tumor drug metabolism is governed by dynamic biological interactions rather than static genetic variants alone. AI-based multi-omics integration therefore provides a promising strategy to capture the complex regulatory networks linking tumor metabolism, genetic alterations, and therapeutic response. Such approaches may improve the predictive accuracy of precision oncology models and support more individualized treatment strategies. AI models can effectively reverse-engineer metabolic states, forecast therapy resistance, and map evolutionary pathways in tumors. By integrating diverse data streams, these systems reveal the regulatory networks governing individual patient responses.

AI-driven drug discovery and personalized medicine: integrating quantum computing and next-generation technologies

Artificial Intelligence in Drug Discovery: Translational Bottlenecks, Biomedical Engineering Constraints, and Commercial Realities

The analysis suggests that AI will become an increasingly important component of pharmaceutical workflows; however, long-term impact will depend less on algorithmic advancement alone and more on effective integration with biological validation, experimental rigor, clinical evidence, and scalable translational infrastructure.

Artificial Intelligence-Driven Natural Product Drug Discovery: From Computational Genome Mining to Clinical Translation.

An operational, end-to-end workflow that explicitly connects computational predictions to medicinal chemistry decision points is provided, addressing a critical gap between computational prediction and clinical translation.

Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges

Modern scientific and technological developments are driving major advances in drug research and development. This narrative review, based on a structured search of PubMed, Scopus, and Web of Science (2018–2026), examines how artificial intelligence (AI) and machine learning (ML) are accelerating a historically prolonged and expensive process, alongside pharmacogenomics, organ-on-a-chip systems, three-dimensional (3D) bioprinting, and nanotechnology. In benchmark studies, deep learning techniques have achieved an area under the receiver operating characteristic curve (AUROC) of over 0.85 for a subset of absorption, distribution, metabolism, excretion, and toxicity (ADMET) endpoints. AI-powered models show promising, albeit platform-dependent, accuracy in predicting candidate drug properties. Pharmacogenomics enables personalized medicine by tailoring therapies according to patients’ genetic profiles, whereas organ-on-a-chip systems and 3D bioprinting provide physiologically relevant human tissue models for preclinical evaluation. In a blinded benchmark study, the Emulate Liver-Chip showed 87% sensitivity and 100% specificity for drug-induced liver injury, outperforming animal models in that specific comparison. Nanotechnology is advancing drug delivery through the use of nanoparticle systems, such as Doxil® and Onpattro®. Obstacles remain, including regulatory constraints, ethical considerations, data quality limitations, and the need for stronger validation, although ongoing funding, interdisciplinary collaboration, and evolving regulatory frameworks may support further development. Overall, these technologies show meaningful potential to shorten development time and improve treatment safety, although further prospective validation is required before realizing this potential at scale.