# PubMed Medical Papers Scraper - 35M+ Citations (`wetyr_corporation/pubmed-medical-papers-scraper`) Actor Bulk extract medical research papers from PubMed/NIH. 35M+ biomedical citations. Search by query, MeSH term, author, journal. Pulls abstracts, DOIs, MeSH classifications. For pharma research and clinical reviews. - **URL**: https://apify.com/wetyr\_corporation/pubmed-medical-papers-scraper.md - **Developed by:** [WETYR CORPORATION](https://apify.com/wetyr_corporation) (community) - **Categories:** Education, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## PubMed Medical Papers Scraper Bulk extract medical research papers from PubMed/NIH (35M+ biomedical citations). Search with full E-utilities query syntax. Pulls abstracts, authors, DOIs, MeSH classifications, journal info. ### Pricing - $0.05 per actor start - $0.005 per paper extracted Typical run: 500 GLP-1/obesity papers with abstracts = **~$2.55**. ### Use cases - Pharma R&D literature reviews - Clinical trial design research - Medical AI / healthcare RAG knowledge bases - Systematic review prep - Drug target discovery research - Healthcare content marketing source material ### Search syntax Use PubMed's powerful E-utilities query language: - `GLP-1[Title]` - title contains GLP-1 - `obesity[MeSH]` - MeSH-tagged - `Smith J[Author]` - author search - `NEJM[Journal]` - journal filter - Combine with `AND`, `OR`, `NOT` Example: `GLP-1[Title] AND obesity[MeSH] AND 2024:2026[PDat]` ### NCBI rate limits Without API key: 3 requests/sec (we throttle automatically). With API key (free at ncbi.nlm.nih.gov/account/settings/): 10 req/sec, faster runs. # Actor input Schema ## `queries` (type: `array`): PubMed E-utilities query strings. Supports field tags like [Title], [Author], [MeSH Terms], [Journal]. Example: 'GLP-1[Title] AND obesity[MeSH]'. ## `dateRange` (type: `string`): Restrict to recent papers. ## `maxResultsPerQuery` (type: `integer`): PubMed allows up to 10000 per query. ## `fetchAbstracts` (type: `boolean`): Pull the full abstract text for each paper. ## `ncbiApiKey` (type: `string`): Get a free key for higher rate limits at ncbi.nlm.nih.gov/account/settings/. Without key: 3 req/sec. With key: 10 req/sec. ## Actor input object example ```json { "queries": [ "GLP-1 receptor agonists obesity" ], "dateRange": "last_5_years", "maxResultsPerQuery": 50, "fetchAbstracts": true } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "queries": [ "GLP-1 receptor agonists obesity" ] }; // Run the Actor and wait for it to finish const run = await client.actor("wetyr_corporation/pubmed-medical-papers-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "queries": ["GLP-1 receptor agonists obesity"] } # Run the Actor and wait for it to finish run = client.actor("wetyr_corporation/pubmed-medical-papers-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "queries": [ "GLP-1 receptor agonists obesity" ] }' | apify call wetyr_corporation/pubmed-medical-papers-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=wetyr_corporation/pubmed-medical-papers-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PubMed Medical Papers Scraper - 35M+ Citations", "description": "Bulk extract medical research papers from PubMed/NIH. 35M+ biomedical citations. Search by query, MeSH term, author, journal. Pulls abstracts, DOIs, MeSH classifications. For pharma research and clinical reviews.", "version": "0.1", "x-build-id": "DygbLIjnvUz5d8TVU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/wetyr_corporation~pubmed-medical-papers-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-wetyr_corporation-pubmed-medical-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/wetyr_corporation~pubmed-medical-papers-scraper/runs": { "post": { "operationId": "runs-sync-wetyr_corporation-pubmed-medical-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/wetyr_corporation~pubmed-medical-papers-scraper/run-sync": { "post": { "operationId": "run-sync-wetyr_corporation-pubmed-medical-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "queries": { "title": "Search Queries", "type": "array", "description": "PubMed E-utilities query strings. Supports field tags like [Title], [Author], [MeSH Terms], [Journal]. Example: 'GLP-1[Title] AND obesity[MeSH]'.", "items": { "type": "string" } }, "dateRange": { "title": "Publication Date Filter", "enum": [ "any", "last_year", "last_5_years", "last_10_years" ], "type": "string", "description": "Restrict to recent papers.", "default": "last_5_years" }, "maxResultsPerQuery": { "title": "Max Results Per Query", "minimum": 1, "maximum": 10000, "type": "integer", "description": "PubMed allows up to 10000 per query.", "default": 50 }, "fetchAbstracts": { "title": "Include Abstracts", "type": "boolean", "description": "Pull the full abstract text for each paper.", "default": true }, "ncbiApiKey": { "title": "NCBI API Key (optional)", "type": "string", "description": "Get a free key for higher rate limits at ncbi.nlm.nih.gov/account/settings/. Without key: 3 req/sec. With key: 10 req/sec." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```