rdkit-cheminformatics
RDKit Cheminformatics Toolkit
Overview
RDKit is the standard open-source cheminformatics library for Python, providing comprehensive APIs for molecular parsing, descriptor calculation, fingerprinting, substructure searching, and chemical reactions. This skill walks through a complete compound library profiling and virtual screening workflow — from loading molecules through drug-likeness filtering, similarity screening, and result visualization.
When to Use
- Calculate molecular properties (MW, LogP, TPSA, HBD/HBA) for a compound set
- Screen a library against a reference compound using fingerprint similarity
- Filter compounds by substructure (SMARTS patterns) for functional group analysis
- Assess drug-likeness using Lipinski's Rule of Five or custom filters
- Generate 2D depictions or 3D conformers for downstream docking
- Enumerate chemical libraries using reaction SMARTS (combinatorial chemistry)
- Cluster compounds by structural similarity for diversity analysis
- Standardize and deduplicate molecular datasets (canonical SMILES, InChI)
- Use
datamol-cheminformaticsinstead for a higher-level RDKit wrapper with batching and error handling; useopenbabelinstead for multi-format conversion (MOL2, XYZ, PDB)
Prerequisites
More from jaechang-hits/sciagent-skills
scientific-brainstorming
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
12gene-database
Query NCBI Gene via E-utilities for curated gene records across 1M+ taxa. Retrieve official gene symbols, aliases, RefSeq accessions, summary descriptions, genomic coordinates, GO annotations, and interaction data. Use for gene ID resolution, cross-species queries, and gene function summaries. For sequence retrieval use Ensembl; for expression data use geo-database.
11snakemake-workflow-engine
Python-based workflow management system for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake automatically determines the execution order and parallelism. Supports local, SLURM, LSF, AWS, and Google Cloud execution via profiles; per-rule conda/Singularity environments. Use for bioinformatics NGS pipelines, ML training workflows, and any multi-step file-processing analysis. Use Nextflow instead for Groovy-based dataflow pipelines or when nf-core ecosystem integration is required.
11esm-protein-language-model
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and protein embeddings. Use when designing novel proteins, extracting sequence representations for downstream ML, or predicting structure from sequence. Local GPU or EvolutionaryScale Forge cloud API. For traditional structure prediction use AlphaFold; for small-molecule cheminformatics use RDKit.
11biopython-sequence-analysis
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
11shap-model-explainability
>-
11