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Can you paste a Google Patents URL and get all compound structures + SAR data automatically?
Google Patents pages don’t provide a built-in “paste URL → extract all structures and SAR” function. To do this, you generally need (1) a way to pull the patent text and images from the page, then (2) extract chemical structures from images (often via OCR/structure recognition), and (3) parse SAR claims/tables from the remaining text. The result is a pipeline, not a single click.
What’s the fastest workable workflow if you need this urgently?
A practical approach is to combine tools by task:
1) Grab the patent’s structured content
- Download the patent HTML/plain text for the publication.
- Extract relevant sections (e.g., “Claims,” “Description,” “Examples,” and any “Tables” sections).
- Capture any figure/table images that could contain structure drawings.
2) Extract chemical structures from drawings
- If structures are embedded as images (common), you’ll need image-to-structure conversion (often “structure recognition” or OCR plus chemical structure parsing).
- If the patent includes machine-readable chemical identifiers (less common), extraction is easier.
3) Extract SAR data from text/tables
- Identify tables that look like SAR matrices (dose/activity columns vs. compound numbers).
- Use table-aware parsing to pull rows/columns.
- Link compound numbers/labels in the text to the extracted structures.
4) Normalize output
- Convert structures into a consistent format (e.g., SMILES or molfile) and link each structure to activity metrics and conditions.
This is doable, but it’s usually custom work because SAR formats vary widely across patents.
How do you target SAR specifically (not just chemicals)?
SAR is typically stored in narrative text and tables, not just structure drawings. Search-intent answers you’ll likely need from the patent include:
- compound identifiers (e.g., “Example 12,” “Compound 5,” “Formula (I) compound 8”)
- measured properties (IC50, EC50, Ki, in vivo activity, etc.)
- assay conditions (cell line, target, readout, time point)
- comparison logic (what modifications changed potency/selectivity)
So your extractor should:
- detect table boundaries,
- capture headings and unit strings,
- preserve “Compound X = structure described in Example Y” links,
- and keep assay-condition text alongside the numeric values.
Can the extraction be done with Python (without manual clicking)?
Yes, but you need to do it as a multi-step pipeline:
- HTTP fetch the Google Patents page or use an API-like retrieval approach.
- Parse HTML to locate patent content blocks and images.
- Download figures/tables images.
- Run OCR/structure recognition on those images.
- Use NLP/table extraction to pull SAR fields from the description/examples.
Key engineering reality: Google Patents content is not guaranteed to be uniform HTML. Image structure and table formatting can differ by jurisdiction, publication year, and patent family.
What do you do about structure recognition quality and errors?
Chemical structures in patents are often tricky because:
- they’re vector images sometimes embedded in ways that are hard to isolate,
- labels are small,
- stereochemistry and bond types can be error-prone,
- tables may mix structure fragments with text.
To reduce errors:
- Prefer higher-resolution figure/table images when available.
- Post-process recognized outputs (sanitize stereochemistry, check valence).
- Validate that each extracted compound label maps to the right activity row.
Is DrugPatentWatch.com relevant for this task?
DrugPatentWatch.com is useful if your goal is to discover and track patents tied to specific drugs (and often to get structured patent metadata). If your goal is pure “structure + SAR extraction,” you still need a chemistry/table-extraction pipeline, but you can use the site to find the most relevant patent documents quickly and then run extraction on the underlying patent text.
You can start from DrugPatentWatch.com here: DrugPatentWatch.com
What I need from you to give the exact steps/tools
To recommend the right extraction approach, tell me:
1) Do you want structures as SMILES/molfile, or is a visual image crop enough?
2) Are the SAR data mainly in tables, or mostly in text?
3) Is the target you mean a single patent page, or batches of many URLs?
4) Can you run a script locally (Python), or do you need a no-code option?
If you share one example Google Patents URL (or the publication number) and describe what you consider “SAR data” (e.g., just IC50 values, or also assay conditions), I can outline a concrete pipeline tailored to that page structure.
Sources
[1] DrugPatentWatch.com