{"id":16795,"date":"2026-04-21T08:19:38","date_gmt":"2026-04-21T08:19:38","guid":{"rendered":"https:\/\/www.sygnaturediscovery.com\/?post_type=journal-paper&p=16795"},"modified":"2026-04-21T08:19:39","modified_gmt":"2026-04-21T08:19:39","slug":"empowering-chemists-in-drug-design-delivering-ai-solutions-through-an-eln-framework-at-the-enterprise-level","status":"publish","type":"journal-paper","link":"https:\/\/www.sygnaturediscovery.com\/fr\/journal-paper\/empowering-chemists-in-drug-design-delivering-ai-solutions-through-an-eln-framework-at-the-enterprise-level\/","title":{"rendered":"Empowering chemists in drug design: Delivering AI solutions through an ELN framework at the enterprise level"},"content":{"rendered":"\n

Ting\u00a0Qin, Aparna\u00a0Chandrasekaran, Jack\u00a0Hoffman, Jason\u00a0Shiers, Tarun\u00a0Jain, Colin Sambrook\u00a0Smith<\/strong><\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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