Introduction

Artificial intelligence is at the centre of attention due to its potential benefits in drug discovery, such as reduced costs, shortened timelines and risk mitigation. AI models can analyze vast datasets to predict drug-target interactions, pharmacokinetics and toxicity, significantly accelerating lead identification, optimization and clinical trial design.¹

Nevertheless, platforms must align with strict regulatory standards to ensure data integrity, patient safety and ethical compliance to fully leverage artificial intelligence for predicting drug efficacy, toxicity and safety. Regulatory bodies like the FDA and EMA have established several frameworks addressing strict information management criteria. These frameworks include:²

• 21 CFR Part 11: Emphasis on the reliability and integrity of electronic records
• FAIR Principles: Findable, Accessible, Interoperable and Reusable
• ALCOA and ALCOA+: Data management must comply with GMP (Good Manufacturing Practice), Attributable, Legible, Contemporaneous, Original and Accurate
• The Annex 11 (EU EMA): Emphasis on software and platform validation, audit trails and electronic signatures
• API (Application Programming Interface) of computerized platforms must maintain secure data exchange, traceability, documentation and version control
• The OQ (Operational Qualification): It addresses the importance of robustness and reproducibility of computer platforms

In light of the plethora of regulatory frameworks, drug innovators deploying AI platforms must demonstrate transparency in how the algorithms are trained and validated and how they make decisions.

Understanding the AI Regulatory Framework in Drug Discovery

An AI regulatory framework comprises a set of principles, guidelines and requirements that govern the development, validation, deployment and monitoring of AI systems in drug discovery and development. The primary goal is to ensure data integrity and transparency while supporting ethical considerations.³

Compared to traditional drug development compliance frameworks, AI regulatory frameworks include additional documentation requirements, such as:⁴

• Training data
• Decision logic
• Algorithm versions
• Validation data
• The existence of black-box models

These strict frameworks ensure the scientific validity and reproducibility of insights generated from AI algorithms while monitoring their clinical implications. Failure to adhere to these frameworks may result in false conclusions, a lack of credibility and ethical violations, undermining AI's value in accelerating drug development.⁴

FDA Guidelines for AI in Drug Development

The FDA has issued a series of guidelines to ensure that AI technologies meet safety, transparency and ethics standards.⁵ Some examples are:

• Artificial Intelligence and Machine Learning Software as a Medical Device (SaMD) Action Plan
• Good Machine Learning Practice for Medical Device Development: Guiding Principles
• Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles
• Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles
• Final Guidance: Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions
• Draft Guidance: Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations

While the principles in these guidelines focus on medical devices, they apply to other AI applications in diagnostics and drug discovery. The key considerations can be summarized as follows:⁶

• The purpose of the AI platform must be clearly outlined (e.g., diagnosis, target prediction)
• The potential impact or risks on patient safety must be disclosed
• The input data, training workflow, performance metrics and the decision-making logic must be explained
• The accuracy of the model must be demonstrated and documented
• All modifications and recalibration must be included in the report
• Protection measures for sensitive proprietary and patient information must be indicated

Pharma companies and research bodies deploying AI platforms in their pipelines should establish a regulatory strategy early in the lifecycle by strengthening data governance and ensuring model explainability throughout development. Such a meticulously crafted action plan requires cross-disciplinary collaboration among data scientists, clinicians and regulatory experts.⁶

Key Compliance Challenges in AI-Driven Drug Discovery

Although the rules laid out by regulatory frameworks sound straightforward, pharmaceutical companies and research facilities face several challenges when fulfilling them.

Data Quality and Integrity Issues

The accuracy of AI models relies heavily on the quality and consistency of training data. Incomplete, biased or poorly annotated datasets can lead to unreliable predictions, jeopardizing regulatory compliance. Companies should implement robust data standardization and provenance to ensure that regulatory agencies can easily trace the origins and evolution of data.¹

Algorithm Validation and Reproducibility

Validating AI algorithms requires rigorous testing to confirm accuracy and robustness under diverse conditions. Reproducibility of model predictions remains a hurdle, especially for models built on proprietary architectures. Therefore, companies must comprehensively document datasets, model parameters and version control to establish reproducibility and gain scientific credibility.⁷

Ethical Considerations

Ethical challenges in AI-driven drug discovery pose significant obstacles to getting approval. Biases embedded in training datasets, such as the underrepresentation of specific patient populations, skew the generalizability of predictions, diminishing full clinical potential.¹ Furthermore, protecting patient privacy remains a concern, particularly when analyzing patient-specific multi-omics data and digital health records. Researchers must ensure that data are made anonymous and stored securely.⁸

Transparency and Explainability

To cultivate trust between developers, regulators and patients, how AI models use and interpret data must be clear. AI frameworks encourage the development of explainable models and governance mechanisms that monitor bias and contradictory predictions throughout the drug development lifecycle. Explainability enhances accountability and streamlines regulatory review by clarifying the link between model inputs, outputs and clinical relevance.⁹

Best Practices for Navigating Regulatory Challenges

As frameworks for AI regulatory compliance continue to evolve, maintaining compliance for AI in drug discovery requires proactive engagement with regulatory agencies. Pharma organizations should align data acquisition, model training and validation processes with Good Machine Learning Practice (GMLP) principles. The entire workflow must be documented according to the proposed guidelines. In that regard, early regulatory consultation and continuous dialogue with authorities can help anticipate requirements and improve chances of approval.⁵

Successful AI regulatory compliance also requires collaboration between AI developers, data scientists, clinicians and regulatory professionals. Collaboration promotes the alignment of technological innovation with regulatory expectations on patient safety and ethical standards.¹

For AI-driven drug discovery, regulatory compliance should transcend market authorization. AI platforms should undergo continuous performance monitoring, periodic revalidation and documentation updates even after the product's regulatory approval. Implementing a lifecycle management framework with defined updating procedures helps maintain long-term compliance.¹⁰

References

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