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xche-yds/agenticmedchem

Analysis updated 2026-05-18

28PythonAudience · researcherComplexity · 4/5Setup · moderate

TLDR

A multi-agent Python pipeline that extracts chemical structures and drug data from research papers into a database.

Mindmap

mindmap
  root((AgenticMedChem))
    What it does
      Extracts data from drug papers
      Uses parallel AI agents
      Builds SQLite database
    Tech stack
      Python
      Claude CLI
      SQLite
      Optical recognition
    Use cases
      Extract chemical structures
      Pull biological measurements
      Find structure-activity patterns
    Audience
      Drug researchers
      Med chem scientists

Code map

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What do people build with it?

USE CASE 1

Extract chemical structures from PDF tables and convert drawings into SMILES text format.

USE CASE 2

Pull biological measurements like IC50 potency values out of scientific papers automatically.

USE CASE 3

Identify structure-activity relationship patterns across a set of drug candidate papers.

USE CASE 4

Process PROTAC and molecular glue papers by breaking bifunctional molecules into parts.

What is it built with?

PythonClaude CLISQLiteSMILES

How does it compare?

xche-yds/agenticmedchemalicankiraz1/codexqbcrain99/worldcut-2026
Stars282828
LanguagePythonPythonPython
Setup difficultymoderateeasymoderate
Complexity4/53/53/5
Audienceresearcherdevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 1h+

Requires a Claude CLI Max subscription, source PDFs and data are not included in the repo.

In plain English

This repository is a Python pipeline that reads scientific papers and patents about drug development and extracts the useful data into a structured database. Drug researchers publish papers full of tables showing how different chemical compounds performed in lab tests, what their structures look like, how they were synthesized, and which proteins they target. Pulling all that information out by hand is slow and error-prone. This project automates the process using a team of AI agents that each handle a specific part of the job. The pipeline works in stages. First, it loads a PDF and renders its pages as images. Then several agents run at the same time: one extracts the chemical structures of compounds (expressed as SMILES, a text format that encodes molecular geometry), another pulls out the biological measurements like IC50 values (a standard way to express how potent a drug candidate is), a third reads synthesis routes, and a fourth identifies the protein targets. For dense tables of chemical structures that are drawn as images rather than text, the system uses optical recognition tools to convert those drawings into the SMILES format. After the parallel extraction phase, a reasoning agent identifies structure-activity relationships, meaning patterns in how small changes to a molecule's structure change its potency. A reviewer agent then re-reads the original PDF to check for errors or hallucinations in what the other agents produced. Results go into a SQLite database. The pipeline also handles a specialized category of drug molecules called PROTACs and molecular glues, which work by recruiting cellular machinery to destroy a disease-causing protein rather than just blocking it. Separate agents break these bifunctional molecules into their component parts. All AI calls go through the Claude CLI using a Max subscription rather than a pay-per-use API, so the cost is a flat monthly fee regardless of how many papers you process. The repository contains only code, source PDFs and extracted data are excluded from version control.

Copy-paste prompts

Prompt 1
Explain how the parallel extraction agents divide up work on a single PDF.
Prompt 2
Show me how SMILES structures are recognized from chemical structure drawings in this pipeline.
Prompt 3
Help me set up the Claude CLI Max subscription so I can run this on my own papers.
Prompt 4
Walk me through how the reviewer agent checks for hallucinations in the other agents' output.

Frequently asked questions

What is agenticmedchem?

A multi-agent Python pipeline that extracts chemical structures and drug data from research papers into a database.

What language is agenticmedchem written in?

Mainly Python. The stack also includes Python, Claude CLI, SQLite.

How hard is agenticmedchem to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is agenticmedchem for?

Mainly researcher.

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