arXiv:2602.03554v2 Announce Type: replace-cross Abstract: Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evaluation of retrosynthesis performance remains limited. Existing benchmarks and metrics typically rely on published synthetic procedures and Top-K accuracy based on single ground-truth, which does not capture the open-ended nature of real-world synthesis planning. We propose a new benchmarking framework for single-step retrosynthesis that evaluates both general-purpose and chemistry-specialized LLMs
The rapid advancement of large language models (LLMs) is pushing their application into complex scientific domains like drug discovery, necessitating more sophisticated evaluation methods.
Improved objective evaluation of LLMs in synthesis planning directly impacts the efficiency and reliability of drug discovery and materials science, accelerating innovation in those fields.
The proposed benchmarking framework allows for a more nuanced and realistic assessment of LLM capabilities in retrosynthesis, moving beyond simplistic single-answer metrics to better reflect real-world problem-solving.
- · AI drug discovery platforms
- · Pharmaceutical companies
- · Chemical R&D
- · Open-ended AI research
- · Developers relying on simplistic evaluation metrics
- · Traditional retrosynthesis methods
More accurate and robust AI models for predicting chemical synthesis pathways will be developed.
Accelerated discovery of new drugs and materials due to improved synthetic efficiency and reduced experimental cycles.
Enhanced automation in chemistry labs, potentially leading to fully autonomous chemical discovery systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL