Back to Home
🤖
Free
🤖Data & AI
✓ Verified

Graph-based drug discovery toolkit (ML + Ai-ml)

Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.

K-Dense-AI

Core Features

Ready to Use

Quick integration into your workflow with minimal setup

Community Verified

Active open-source community with continuous updates

Completely Free

MIT/Apache licensed for commercial and personal use

Flexible Extension

Customizable and extendable based on your needs

How to Use

1Get Skill File

Download or copy the skill file from the source repository

Visit Source Repository

2Install to Claude

Place the skill file in Claude's skills directory (usually ~/.claude/skills/)。

3Start Using

Restart Claude or run the reload command to load the skill

Tip: Read the documentation and code carefully before first use to understand functionality and permission requirements

Technical Information

Author
K-Dense-AI
Category
Data & AI
File Size
13.66 KB
Source Repository
K-Dense-AI__claude-scientific-skills
Metadata
Includes YAML metadata
License
MIT
Download SkillView GitHub Source

All Skills from open-source community, preserving original authors' copyrights

K-Dense-AI__claude-scientific-skills/scientific-skills/torchdrug/SKILL.md

Related Skills

Similar skills recommended based on tags and categories

Analysis toolkit for machine learning
🤖Data & AI
Claudeskillsfree

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Author:K-Dense-AI
Analysis toolkit for machine learning (GO + ML)
🤖Data & AI
Claudeskillsfree

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Author:K-Dense-AI
Analysis toolkit for machine learning (GO + ML) v2
🤖Data & AI
Claudeskillsfree

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Author:K-Dense-AI
Analysis toolkit for data analysis (Python + Ai-ml)
🤖Data & AI
Claudeskillsfree

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

Author:K-Dense-AI