# R-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing

arXiv: https://arxiv.org/abs/2610.00700 · HTML: https://mlsanity.com/paper/2610.00700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [PathVQA: 30000+ Questions for Medical Visual Question Answering](https://mlsanity.com/paper/2003.10286/) (2020)
- [Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations](https://mlsanity.com/paper/2505.21318/) (2025)
- [MolGrapher: Graph-based Visual Recognition of Chemical Structures](https://mlsanity.com/paper/2308.12234/) (2023)
- [MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild](https://mlsanity.com/paper/2411.11098/) (2024)
- [MolLangBench: A Comprehensive Benchmark for Language-Prompted Molecular Structure Recognition, Editing, and Generation](https://mlsanity.com/paper/2505.15054/) (2025)
- [OCSU: Optical Chemical Structure Understanding for Molecule-centric Scientific Discovery](https://mlsanity.com/paper/2501.15415/) (2025)
- [MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures](https://mlsanity.com/paper/2503.16096/) (2025)
- [textttPatentAgent: Intelligent Agent for Automated Pharmaceutical Patent Analysis](https://mlsanity.com/paper/2410.21312/) (2024)
- [MarkushGrapher-2: End-to-end Multimodal Recognition of Chemical Structures](https://mlsanity.com/paper/2603.28550/) (2026)

## Abstract (arXiv)

Recent advances in AI for scientific discovery enable molecular understandingand design, yet reasoning over incomplete chemical representations remainsunclear.Markush structures, which encode molecular families through variable R-groupplaceholders (textitRtextsubscript1, textitRtextsubscript2, textitX, etc.), are ubiquitous in pharmaceutical patents and requiregrounding across molecular, textual, and chemical information.However, existing molecule-language benchmarks focus on fully specifiedmolecules, leaving R-group grounding largely unevaluated.We introduce R-GroundBench:, a diagnostic benchmark built from real patent Markushstructures, featuring a Multiple-Choice (VQA) track with controlled difficultyand modality splits, and an open-ended Generation track.Our results reveal a substantial gap between recognition andmolecular grounding.While models achieve over 90% accuracy on Easy VQA, performance drops to56--66% on Hard VQA when shortcuts are controlled.Chemical-domain VLMs also remain unreliable, achieving only 25.7--46.2% on HardVQA despite domain-specific pretraining.Moreover, Generation Exact Match remains below 20% for most models and below8% when visual input is required.These findings reveal that current AI systems lack reliable grounding andexecution for Markush editing, highlighting challenges for AI-drivenscientific discovery.