chematagent-drug-discovery

$npx mdskill add swaruplab/operon/chematagent-drug-discovery

Designs molecules and plans synthesis routes for drug discovery.

  • Generates novel molecular structures with desired chemical properties.
  • Uses 137 Python-wrapped chemical tools for modeling and prediction.
  • Filters candidates by predicted ADMET properties like solubility and toxicity.
  • Outputs synthesis recipes and instructions for robotic lab platforms.

SKILL.md

.github/skills/chematagent-drug-discoveryView on GitHub ↗
---
name: chematagent-drug-discovery
description: Chemical Lab Agent
keywords:
  - chemistry
  - drug-discovery
  - tools
  - synthesis
  - property-prediction
measurable_outcome: Plan a synthesis route and predict ADMET properties for a candidate molecule with >80% validity.
license: MIT
metadata:
  author: CheMatAgent Team
  version: "1.0.0"
compatibility:
  - system: Python 3.9+
allowed-tools:
  - run_shell_command
  - read_file
---

# CheMatAgent

A two-tiered agent system with access to 137 Python-wrapped chemical tools for drug discovery and materials science.

## When to Use

*   **Molecule Design**: Generating novel structures with specific properties.
*   **Property Prediction**: Estimating solubility, toxicity, and bioactivity.
*   **Synthesis Planning**: Designing retro-synthetic routes.

## Core Capabilities

1.  **Tool Orchestration**: Manages a library of 137 chemical tools.
2.  **Multi-Scale Modeling**: Bridges quantum mechanics and molecular dynamics.
3.  **Lab Automation**: Generates instructions for robotic synthesis platforms.

## Workflow

1.  **Goal**: Define target property (e.g., "LogP < 5").
2.  **Design**: Generate candidates.
3.  **Filter**: Use property prediction tools.
4.  **Plan**: Output synthesis recipe.

## Example Usage

**User**: "Design a molecule similar to Aspirin but with higher solubility."

**Agent Action**:
```bash
python -m chematagent.design --scaffold "Aspirin" --objective "maximize solubility"
```

More from swaruplab/operon

SkillDescription
agentd-drug-discoveryUse the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.
alphafold-databaseAccess AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
antibody-design-agentAn advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion).
archrSingle-cell ATAC-seq analysis with ArchR (R). The mature R-based scATAC pipeline — Arrow files, doublet inference, iterative LSI + Harmony, clustering, gene scores, MACS2 peak calling, motif enrichment, chromVAR deviations, footprinting, scRNA-seq integration (label transfer), trajectory analysis (built-in + Monocle3 + Slingshot), and ArchR's interactive genome browser. Sister protocol to snapatac2 — pick this for established R workflows.
automated-bioinformatics-pipelinesPipeline maestro
autonomous-biomedical-agentAutonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
autonomous-oncology-agentPrecision Oncology
basecallingConvert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Note: Guppy is deprecated; use Dorado for all new analyses.
batch-processingProcess multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.
bindingdb-databaseQuery BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.