Beyond Structure Recognition: Benchmarking Patentability Assessment in Drug Discovery

Assessing patentability in drug discovery requires more than recognizing chemical structures: models must interpret the scope jointly defined by structural drawings and claim language, preserve its critical limitations, and assess it against relevant evidence. Markush claims make this task particularly challenging, as a shared scaffold, variable substituents, and textual restrictions jointly define a family of compounds. We introduce **MarkPatentBench**, a multimodal benchmark for assessing the patentability of small-molecule drug claims, curated from patent examination records of the European and U.S. patent office websites. The benchmark contains claim text and Markush chemical structure images with source-linked examination reference documents. It supports overall patentability prediction and assessment generation across five core dimensions: novelty, inventive step, support by the description, claim clarity, and patent-eligible subject matter. Human domain experts review the benchmark for data quality and annotation reliability. We evaluate existing large language and vision-language models under claim-only, evidence-augmented, and agentic configurations. Our analysis highlights potential reasoning failures, including overlooking claim limitations, inferring obviousness from scaffold familiarity, and treating missing information as evidence of a substantive defect. **MarkPatentBench** provides a testbed for investigating whether models can move beyond structure recognition to preserve the claimed chemical scope and support their patentability assessments with applicable evidence.