Bioinformatics Pipeline Validator for Drug Discovery
Validate computational biology workflows before expensive drug discovery simulations run
What You Can Do
You can translate experimental designs and published methodologies into validated, executable Python bioinformatics pipelines with built-in error detection. This skill reviews your computational biology code to ensure it correctly implements your intended logic, catches common mistakes like tool parameter mismatches and data format errors, and documents workflows for reproducibility—all before running costly molecular docking, structure prediction, ADMET modeling, or molecular dynamics simulations.
Features
verifies that your code correctly implements the computational methods and scientific logic you intended
confirms input/output formats match between pipeline stages and match tool requirements
detects incorrect tool parameters, threshold values, and configuration settings that produce wrong results
identifies violations of statistical assumptions, molecular physics constraints, and domain-specific requirements
generates parameter tracking, assumptions logging, and workflow documentation for peer review and replication
flags typical bioinformatics mistakes like unit mismatches, coordinate system errors, and sequence format incompatibilities
validates entire drug discovery workflows (ligand prep → docking → ADMET → scoring) for stage-to-stage consistency
confirms your code matches the methodology described in published papers or your protocol
Example Output
Example 1: Molecular Docking Pipeline Validation
Input: Your Python script for AutoDock Vina molecular docking workflow
Output:
- ✅ Ligand preparation correctly uses RDKit to remove waters and add hydrogens
- ⚠️ Grid box coordinates specify 40Å sides but your compound is 15Å—increase specificity to 20Å
- ❌ ADMET scoring uses pKa values in wrong pH range (pH 7.4 vs pH 2.0)
- 📋 Reproducibility checklist: seed values (fixed), tool versions (specify versions), parameter log (generated)
Example 2: Sequence Analysis Pipeline
Input: FASTA processing and alignment pipeline for genomic variant analysis
Output:
- ✅ Multiple sequence alignment preserves gap handling across 500 sequences
- ⚠️ Statistical test assumes normal distribution but variant counts are Poisson—recommend binomial test instead
- ❌ Output coordinates reference hg38 but input file uses hg19—mismatch detected
- 📋 Validated stages: quality filtering (pass), alignment (pass), annotation (needs review)
Example 3: Molecular Dynamics Simulation
Input: GROMACS/AMBER workflow for 100ns protein folding simulation
Output:
- ✅ Force field selection (AMBER99SB) matches protein type and validated in literature
- ⚠️ Simulation timestep 2fs is appropriate but equilibration only 10ps—recommend 100ps minimum
- ❌ Solvent box density 1.2 g/cm³ is unrealistic (water = 1.0)—check parameters
- 📋 Performance estimate: ~48 GPU hours on A100, memory requirement ~8GB
What's Included
- SKILL.md: complete skill instruction file with prompts and validation frameworks
- Pipeline Validation Checklist: step-by-step checklist for reviewing computational biology workflows (logic, data schemas, parameters, assumptions)
- Common Bioinformatics Errors Reference: catalog of typical mistakes in molecular docking, sequence analysis, structure prediction, ADMET modeling, and MD simulations with corrections
- Reproducibility Documentation Template: parameter logging, assumptions tracking, and workflow annotation framework
- Method-to-Code Mapping Framework: template for translating published methodology sections into executable, validatable code
Who It's For
- Computational Biologists — validating Python/R pipelines before submitting to HPC clusters or cloud compute
- Drug Discovery Research Scientists — catching expensive simulation errors before running molecular docking or MD workflows
- Bioinformatics Engineers — reviewing complex multi-stage analysis pipelines for configuration bugs and logic errors
- Academic Researchers — translating published methods into reproducible code with documentation for peer review
- Biotech Software Developers — validating that custom tools correctly implement standard molecular biology algorithms
Best For
- Molecular docking workflows (ligand prep, grid setup, docking, scoring)
- Sequence analysis and alignment pipelines (QC, alignment, variant calling, annotation)
- ADMET property prediction and scoring model validation
- Molecular dynamics simulation setup and trajectory analysis
- Structure prediction pipeline validation (homology modeling, AlphaFold workflows)
- Multi-stage drug discovery workflows with inter-stage data dependencies
- Converting published methods and protocols into executable code
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