arXiv:2606.13477v1 Announce Type: cross Abstract: Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications. However, designing host-guest systems remains time-consuming, requiring days of dry-lab verification per candidate pair. Although LLMs have emerged as a fast alternative with strong performance on molecular binding tasks, no benchmark currently systematically evaluates LLMs for host-guest reasoning across fundamental supramolecular chemistry tasks, e.g., binding affinity prediction. To this end, we collaborate with domain
The proliferation of advanced LLMs necessitates specialized benchmarks to evaluate their capabilities in highly technical domains like supramolecular chemistry, pushing the boundaries of AI application.
This benchmark directly addresses the need for rigorous evaluation of AI's performance in complex scientific design, potentially accelerating drug discovery, materials science, and other applications of supramolecular chemistry.
LLMs can now be systematically tested and improved for their ability to predict and design host-guest systems, moving beyond general molecular binding tasks to specific, intricate chemical reasoning.
- · AI researchers specializing in scientific discovery
- · Pharmaceutical companies
- · Materials science industry
- · AI model developers
- · Traditional, time-consuming dry-lab verification methods
The new benchmark accelerates the development of LLMs tailored for supramolecular chemistry tasks.
Faster and more efficient design of novel host-guest systems leads to breakthroughs in drug delivery, sensors, and sustainable materials.
The success in supramolecular chemistry sets a precedent for AI's systematic application and benchmarking in other complex chemical and biological domains, revolutionizing scientific R&D.
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Read at arXiv cs.CL