arXiv:2606.23745v1 Announce Type: cross Abstract: We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representations of active ligands. JEDEL is the first model to map pharmacophore interaction patterns to actionable, scalable synthesis instructions, enabling the design of targeted libraries comprising potentially millions of molecules. Unlike existing generative approaches that produce virtual compounds requiring downstream synthesis planning, JEDEL operates within the space of purchasable building blocks and
The convergence of advanced AI with biological research, specifically in drug discovery, is rapidly progressing, driven by computational power and data availability.
This development represents a significant acceleration in early-stage drug discovery, potentially reducing timelines and costs for identifying promising drug candidates by enabling highly targeted and synthesizable compound libraries.
Drug discovery workflows shift from iterative virtual compound generation to direct synthesis-ready library design, minimizing unproductive early-stage efforts and leveraging purchasable building blocks more effectively.
- · Pharmaceutical companies
- · Biotechnology startups
- · AI-driven drug discovery platforms
- · Chemical suppliers
- · Traditional high-throughput screening services
- · Companies reliant on older, less efficient drug discovery methods
Faster and more cost-effective identification of lead compounds for various diseases.
Increased pipeline diversity and success rates for drug development, leading to new therapeutic options.
Potential for 'on-demand' drug design and rapid response to emerging health threats, profoundly altering the pharmaceutical industry's structure.
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Read at arXiv cs.AI