Search the ZINC database by ZINC ID, SMILES, and supplier codes.
npx skills add https://github.com/robinbarvaag/poynt --skill zinc-database-robinbarvaag
Please help me install this Agent Skill. Skill: zinc-database Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/zinc-database Command: npx skills add https://github.com/robinbarvaag/poynt --skill zinc-database-robinbarvaag
This Skill provides access to the ZINC database, a vast collection of purchasable chemical compounds, enabling researchers to find molecules for virtual screening, lead discovery, and drug development.
Use the zinc-database skill to search for compounds similar to 'CC(C)Cc1ccc(cc1)C(C)C(=O)O' with a Tanimoto distance of 3.
High-intent search queries and answers about installing and using this skill.
You can search the ZINC database for chemical compounds by providing a SMILES string to find specific molecules or discover similar structures for virtual screening and lead discovery.
Yes, you can download ready-to-use 3D molecular structures from the ZINC database to facilitate molecular docking and virtual screening for drug discovery.
To find commercially available analogs, perform a similarity search in the ZINC database using the SMILES string of a known active compound to retrieve purchasable molecules.
You can retrieve a specific compound by searching the ZINC database using its unique ZINC ID, which provides direct access to the chemical compound's details and 3D structures.
Yes, the ZINC database supports random sampling of chemical space, enabling researchers to explore diverse molecular structures for lead discovery and virtual screening.
Yes, the ZINC database supports searching for chemical compounds by supplier codes, allowing you to retrieve specific purchasable molecules for drug development.
biomaps-infraSearch and retrieve chemical compounds from the ZINC database by ID, SMILES, or supplier codes.
K-Dense-AIQuery ZINC22 for purchasable compounds and download 3D structures for docking.
InternScienceCompute topological descriptors like TPSA and Chi indices for SMILES lists.
cxnaiveEvolve prompts, regex patterns, SQL queries, and code snippets via LLM-driven search.
