How are compounds categorized in ZINC?

Compounds are organized into 'tranches' based on hydrogen bond donors, LogP, and molecular weight, which this skill can help you navigate.

Can I perform similarity searches for new drug leads?

Yes, the skill allows you to search by SMILES string with adjustable distance parameters to find structural analogs of known hit compounds.

Does this skill provide 3D molecular structures?

It provides the metadata and repository paths to download 3D-ready structures in formats like MOL2 and SDF, optimized for docking tools like DOCK.

What version of ZINC does this skill support?

This skill primarily focuses on ZINC22, the most current and comprehensive version containing over 230 million purchasable compounds.

ZINC Database Search & Discovery

Name: ZINC Database Search & Discovery
Author: Microck

byMicrock

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Ciencia de Datos y ML

Accesses the ZINC22 database to search, filter, and retrieve 230M+ purchasable chemical compounds for virtual screening and drug discovery.

Acerca de

This skill integrates Claude with the ZINC22 repository, allowing researchers and developers to navigate a massive database of over 230 million purchasable compounds. It enables advanced chemical searches via ZINC IDs or SMILES strings, supports Tanimoto similarity searches for analog discovery, and provides access to 3D-ready structures for molecular docking. By automating API interactions with CartBlanche22, this skill streamlines the process of building screening libraries, exploring chemical space, and identifying lead-like or drug-like molecules directly within a scientific coding or research workflow.

Características Principales

Search 230M+ compounds by ZINC ID or SMILES notation
Perform Tanimoto similarity searches for analog discovery
Filter molecules by properties like LogP, MW, and H-bond donors
Retrieve 3D-ready structures for molecular docking simulations
Sample chemical subsets including fragments and drug-like molecules
81 GitHub stars

Casos de Uso

Preparing virtual screening libraries for target-based drug discovery
Identifying commercially available analogs for hit-to-lead optimization
Sampling diverse chemical space for benchmarking screening algorithms

Acerca de

Características Principales

Search 230M+ compounds by ZINC ID or SMILES notation
Perform Tanimoto similarity searches for analog discovery
Filter molecules by properties like LogP, MW, and H-bond donors
Retrieve 3D-ready structures for molecular docking simulations
Sample chemical subsets including fragments and drug-like molecules
81 GitHub stars

Casos de Uso

Preparing virtual screening libraries for target-based drug discovery
Identifying commercially available analogs for hit-to-lead optimization
Sampling diverse chemical space for benchmarking screening algorithms