Revolutionizing Drug Discovery With AI From Target Identification To Post-Marketing Surveillance

Artificial Intelligence; Drug Discovery; Target Identification; Pharmaceutical Safety; Regulatory Compliance.

Kavyashree K R1,
Assistant Professor, Department of College of
Pharmaceutical Sciences, Specialisation:
Pharmaceutical regulatory affairs, Dayananda
Sagar University, Bangalore.
Email id:[email protected],
Orcid id:0009-0000-7207-0997

Harshitha H2,
Lecturer, Department of College of
Pharmaceutical Sciences, Specialisation:
Microbiology, Dayananda Sagar University,
Bangalore.
Email id:[email protected],
Orcid id:0009-0000-6673-9201

Mangala Divya Sree3,
Lecturer, Department of College of
Pharmaceutical Sciences, Specialisation:
Pharmacology, Dayananda Sagar University,
Bangalore.
Email id:[email protected],
Orcid id:0009-0005-5378-7008

Prajwal S P4,
Assistant Professor, Department of Computer
Science and Engineering, Dayanand Sagar
University, Bangalore, Karnataka, India,
Email id:[email protected],
Orcid id:0009-0009-1012-1440

Goutham T R5,
Assistant Professor, Department of Computer
Science and Engineering, Dayananda Sagar
University, Bangalore,
Email id:[email protected],
Orcid id: 0009-0000-5224-0485

Bhagyalaxmi S Banakar6,
Assistant Professor, Department of CSE(Data
Science), Dayanand Sagar University,
Bangalore, Karnataka, India,
Email id:[email protected],
Orcid id:0009-0008-9617-7906

Meghana G S7,
Assistant Professor, Department of
Pharmaceutics, JSS College of Pharmacy,
Mysore, Karnataka, India,
Email id:[email protected],
Orcid id:0000-0003-2384-4081

Received:25-06-2026
Revised: 30-07-2026
Accepted:07-08-2026

European Journal of Prosthodontics and Restorative Dentistry (2026) 34(7s), 1081–1097

Revolutionizing Drug Discovery With AI From Target Identification To Post Marketing Surveillance

Background: Artificial Intelligence (AI) is transforming drug discovery by enhancing efficiency, accuracy, and cost-effectiveness across the entire development pipeline, from target identification to post-marketing surveillance. Since its origins in the 1950s, AI has evolved into a vital tool in pharmaceuticals. Technologies such as Machine Learning (ML) and Deep Learning (DL) have improved our understanding of disease mechanisms, pharmacokinetics, and clinical trial design. Methodology: This review examines AI applications across four major stages of drug discovery: target identification, preclinical development, clinical trials, and post-marketing surveillance. Insights and regulatory perspectives from agencies such as the US FDA, EMA, Health Canada, and other ethical and regulatory bodies are also discussed. Results: AI significantly accelerates drug discovery by rapidly identifying potential targets, predicting ADMET properties, and optimising clinical trial design. It also strengthens post-marketing surveillance through early detection of safety signals. These advancements collectively improve precision, reduce development timelines, and enhance drug safety and efficacy. Conclusion: AI is revolutionising drug discovery by streamlining processes from early target identification to post-approval monitoring. Its ability to support virtual screening, improve drug design, and enhance decisionmaking is expected to lead to safer and more effective therapies, ultimately advancing patient outcomes and innovation in the pharmaceutical industry.

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