Empowering chemists in drug design: Delivering AI solutions through an ELN framework at the enterprise level

Artificial intelligence (AI) holds immense potential to revolutionize drug discovery, yet its widespread adoption within scientific enterprises faces significant hurdles. Key challenges include ensuring user-friendliness, managing complex workflows, and integrating diverse datasets. To address these issues, we propose a novel framework that leverages the familiar Electronic Lab Notebook (ELN) paradigm. By formalizing AI workflows as ELN protocols and treating AI execution as ELN experiments, the proposed system provides a scalable, traceable, and user-oriented deployment strategy that aligns with existing laboratory practices. This ELN-based framework adheres to FAIR principles, enhancing data findability, accessibility, interoperability, and reusability. By mirroring the intuitive ELN interface, our solution empowers bench chemists to easily access and utilize cutting-edge AI tools, enabling them to move beyond purely synthetic roles and fully engage as medicinal chemists. This allows chemists to design compounds with real-time consideration of synthetic feasibility and to actively contribute to the drug design process with their practical expertise, thereby accelerating drug discovery efforts and maximizing the return on AI investments.