Is it possible to generate 3D structures from SMILES?

Absolutely. This skill provides guidance for using the ETKDG method for 3D embedding and applying force fields like MMFF94 to optimize the resulting molecular geometry.

Can this skill handle large chemical datasets?

Yes, the skill includes patterns for using SDMolSupplier and MultithreadedSDMolSupplier, which are optimized for iterating through large SDF and SMILES files without exhausting memory.

What is the difference between RDKit and Datamol?

Datamol is a higher-level wrapper for RDKit designed for standard workflows. This RDKit skill is preferred when you need lower-level control, custom sanitization logic, or advanced algorithmic adjustments.

How does RDKit handle molecular sanitization?

RDKit automatically performs sanitization steps such as valence checking, aromaticity perception, and chirality assignment during parsing, but also allows for manual control over these steps for specialized tasks.

RDKit Cheminformatics

Name: RDKit Cheminformatics
Author: lifangda

bylifangda

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데이터 과학 및 ML

Enables advanced molecular modeling, chemical property calculation, and structural analysis within Python workflows.

소개

The RDKit Cheminformatics skill provides a powerful suite of tools for handling chemical data, from parsing SMILES and SDF files to generating 3D molecular structures. It allows for high-precision molecular manipulation, including substructure searching using SMARTS patterns, calculation of physicochemical descriptors like LogP and TPSA, and the simulation of chemical reactions. This skill is ideal for researchers and developers in drug discovery and computational chemistry who need fine-grained control over molecular sanitization, fingerprinting, and similarity analysis.

주요 기능

Substructure matching and advanced SMARTS pattern querying
Molecular fingerprinting and Tanimoto similarity metrics
2D/3D coordinate generation with UFF/MMFF force-field optimization
Molecular I/O for SMILES, SDF, InChI, and MOL formats
Comprehensive descriptor calculation including MW, LogP, and TPSA
16 GitHub stars

사용 사례

High-throughput screening and drug-likeness filtering using Lipinski's Rules
Building automated chemical reaction pipelines for virtual synthesis
Generating 3D conformer ensembles for molecular docking and modeling

소개

주요 기능

Substructure matching and advanced SMARTS pattern querying
Molecular fingerprinting and Tanimoto similarity metrics
2D/3D coordinate generation with UFF/MMFF force-field optimization
Molecular I/O for SMILES, SDF, InChI, and MOL formats
Comprehensive descriptor calculation including MW, LogP, and TPSA
16 GitHub stars

사용 사례

High-throughput screening and drug-likeness filtering using Lipinski's Rules
Building automated chemical reaction pipelines for virtual synthesis
Generating 3D conformer ensembles for molecular docking and modeling