Artificial Intelligence Enabled Precision Drug Discovery And Development: A Conceptual Framework And Evaluation Roadmap

Abstract

The drug discovery and development process remains protracted, costly, and marked by high attrition rates, with average timelines exceeding a decade and success rates in clinical phases remaining below 12%. This inefficiency is driven by fragmented data sources, limited target tractability, poor translation between preclinical and clinical stages, and challenges in patient stratification. Artificial intelligence (AI), particularly the emergence of multimodal foundation models and predictive analytics, offers a transformative opportunity to reimagine this pipeline. This paper proposes a comprehensive conceptual framework for AI-enabled precision drug discovery and development that systematically integrates chemical, biological, and clinical data using scalable AI architectures. Central to this framework are self-supervised foundation models that learn generalized biomedical representations from diverse modalities, harmonized through standardized ontologies and data models. We detail how these models can support key tasks across the pipeline, including target identification, molecular design, bioactivity prediction, safety profiling, and clinical trial optimization, augmented by real-world biomedical data (RWD) to improve generalizability and relevance. An evaluation roadmap is presented, outlining recommended benchmark datasets, task-specific metrics, and validation strategies across preclinical and clinical contexts. We further highlight implementation enablers such as open-source tools, data harmonization standards (e.g., OMOP CDM), and federated learning infrastructures. Finally, the framework addresses critical governance dimensions including bias mitigation, explainability, model robustness, data privacy, and alignment with regulatory frameworks such as the FDA’s real-world evidence (RWE) guidance. This paper serves as a strategic blueprint for academic researchers, pharmaceutical industry innovators, and regulatory bodies seeking to operationalize responsible and scalable AI in biomedical innovation.