denovo-design
GitHub提供从头药物设计工具包，支持基于骨架、片段、结构的分子生成及多目标优化。涵盖类似物枚举、片段生长链接、SBDD设计及药性过滤，旨在快速筛选和优先排序候选药物分子。
触发场景
安装
npx skills add synthetic-sciences/openscience --skill denovo-design -g -y
SKILL.md
Frontmatter
{
"name": "denovo-design",
"license": "MIT",
"category": "chemistry",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "De novo molecule generation for drug discovery. Scaffold-based analog enumeration, fragment growing\/linking, structure-based design, multi-objective optimization, and drug-likeness filtering."
}
De Novo Molecule Design
Overview
De novo design is the computational generation of novel molecular structures with desired properties, without starting from known active compounds. This skill provides a complete toolkit for generating drug candidates through multiple complementary strategies: scaffold-based analog enumeration, fragment-based design, structure-based design (SBDD), multi-objective optimization, and drug-likeness filtering.
All generation strategies produce molecules with computed physicochemical properties and similarity metrics, enabling rapid prioritization. The scripts are designed for CPU-first execution using RDKit as the core cheminformatics engine, with optional GPU acceleration noted where applicable.
When to Use This Skill
Use this skill when you need to:
- Explore chemical space around a known lead compound by generating analogs with R-group enumeration, bioisosteric replacements, or random mutations
- Design molecules from fragments by growing, linking, or merging fragment hits from screening campaigns
- Generate molecules for a protein target using pocket shape complementarity or pharmacophore constraints
- Optimize a set of hits against multiple objectives (QED, LogP, synthetic accessibility, molecular weight) through iterative refinement
- Filter compound libraries for drug-likeness using Lipinski, Veber, PAINS, Brenk alerts, lead-like, fragment-like, or beyond Rule of Five criteria
- Enumerate focused libraries for virtual screening or synthesis planning
Installation
pip install rdkit-pypi datamol numpy pandas
Optional (for enhanced fragment design and structure-based approaches):
pip install scipy
For structure-based design with PDB parsing:
pip install biopython
Choosing the Right Strategy
| Scenario | Script | Strategy |
|---|---|---|
| Have a lead compound, want analogs | generate_analogs.py |
R-group, bioisostere, mutate |
| Have fragment screening hits | generate_fragments.py |
grow, link, merge |
| Have a protein structure / pocket | generate_sbdd.py |
shape, pharmacophore |
| Have hits, need property optimization | optimize.py |
multi-objective iterative |
| Have a library, need filtering | filter.py |
lipinski, veber, pains, etc. |
Core Workflows
1. Analog Generation
Generate structural analogs of a lead compound using three complementary strategies.
# Generate 50 analogs using all strategies
python scripts/generate_analogs.py --smiles "c1ccc(NC(=O)c2ccccc2)cc1" --output analogs.csv --num 50 --strategy all
# Only bioisosteric replacements
python scripts/generate_analogs.py --smiles "c1ccc(NC(=O)c2ccccc2)cc1" --output analogs.csv --strategy bioisostere
# Only R-group enumeration
python scripts/generate_analogs.py --smiles "c1ccc(NC(=O)c2ccccc2)cc1" --output analogs.csv --strategy rgroup
Strategies:
- R-group enumeration: Identifies aromatic and sp3 carbon positions, enumerates common substituents (methyl, ethyl, halides, CF3, OMe, NH2, OH, CN, etc.)
- Bioisosteric replacement: Swaps functional groups using a curated dictionary (COOH to tetrazole, phenyl to thienyl, amide to sulfonamide, ester to oxadiazole, etc.)
- Random mutation: Fragment-based random modifications to the molecular graph
Output CSV includes: id, smiles, mw, logp, qed, tanimoto_to_parent, strategy.
2. Fragment-Based Design
Build drug-like molecules from fragment hits.
# Grow a fragment by adding substituents
python scripts/generate_fragments.py --fragments "c1cc[nH]c1" --mode grow --output grown.csv --num 50
# Link two fragments
python scripts/generate_fragments.py --fragments "c1cc[nH]c1,c1ccccc1O" --mode link --output linked.csv --num 50
# Merge fragment pharmacophores
python scripts/generate_fragments.py --fragments "c1cc[nH]c1,c1ccccc1O" --mode merge --output merged.csv --num 50
Modes:
- Grow: Adds substituents at attachment points on a single fragment
- Link: Connects two fragments with linkers (alkyl chains, amides, ethers, piperazines, etc.)
- Merge: Combines pharmacophoric features of two fragments into hybrid molecules
3. Structure-Based Design (SBDD)
Generate molecules complementary to a protein binding pocket.
# Shape-based design from a PDB file
python scripts/generate_sbdd.py --protein target.pdb --pocket-residues "ASP189,SER195,HIS57" --method shape --output sbdd_hits.csv --num 100
# Pharmacophore-based design
python scripts/generate_sbdd.py --protein target.pdb --pocket-residues auto --method pharmacophore --output sbdd_hits.csv --num 100
Methods:
- Shape-based (CPU): Generates molecules complementing the pocket shape using fragment assembly
- Pharmacophore (CPU): Defines pharmacophore features from pocket residues and generates matching molecules
Note: For production SBDD, consider GPU-accelerated methods like DiffSBDD or Pocket2Mol. This script provides a CPU-based starting point.
4. Multi-Objective Optimization
Iteratively optimize a set of compounds against multiple property objectives.
# Optimize for QED and LogP with constraints
python scripts/optimize.py --input hits.csv --objectives qed,logp,sa --constraints "mw<500,logp<5,qed>0.5" --output optimized.csv --num-iterations 3
The optimizer runs iterative cycles: generate analogs, compute properties, filter by constraints, rank by multi-objective score, select top compounds for the next round.
5. Drug-Likeness Filtering
Apply standard medicinal chemistry filters to compound libraries.
# Apply Lipinski and PAINS filters
python scripts/filter.py --input library.csv --output filtered.csv --filters lipinski,pains
# Apply all available filters
python scripts/filter.py --input library.csv --output filtered.csv --filters lipinski,veber,qed,pains,brenk,leadlike,fragmentlike,bro5
# Custom thresholds
python scripts/filter.py --input library.csv --output filtered.csv --filters qed,pains --qed-threshold 0.6 --sa-threshold 5.0
Available filters:
- Lipinski Ro5: MW <= 500, LogP <= 5, HBD <= 5, HBA <= 10
- Veber: RotBonds <= 10, TPSA <= 140
- QED: Quantitative drug-likeness score above threshold
- PAINS: Pan-assay interference compounds (RDKit FilterCatalog)
- Brenk: Structural alerts for reactive/toxic groups
- Lead-like: MW 200-350, LogP -1 to 3
- Fragment-like (Ro3): MW <= 300, LogP <= 3, HBD <= 3, HBA <= 3
- bRo5: Beyond Rule of Five for natural product-like space (MW 500-1000, LogP -2 to 10)
- SA score: Synthetic accessibility (1 = easy, 10 = hard)
Tips
- Start broad, then narrow: Use
generate_analogs.pywith--strategy allfirst, then filter withfilter.py, then optimize survivors withoptimize.py. - Fragment merging is powerful: If you have multiple fragment hits from an FBDD campaign, merging can produce leads that retain key interactions from both fragments.
- Combine SBDD with filtering: Structure-based hits often need medicinal chemistry optimization. Pipe SBDD output through
filter.pyandoptimize.py. - Check synthetic accessibility: Always include SA score in your analysis. A beautiful molecule is useless if it cannot be synthesized. Use
--sa-threshold 5.0or lower for practical compounds. - Deduplicate early: All generation scripts deduplicate by canonical SMILES, but if you combine outputs from multiple runs, deduplicate again before downstream analysis.
- Property distributions matter: Look at the property distribution summaries printed by each script. Bimodal distributions or outliers can indicate issues with the generation strategy.
版本历史
- e9844a4 当前 2026-07-11 17:21
