denovo-design

name	denovo-design
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.
category	chemistry
license	MIT
metadata	{"skill-author":"Synthetic Sciences"}

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.py with --strategy all first, then filter with filter.py, then optimize survivors with optimize.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.py and optimize.py.
• Check synthetic accessibility: Always include SA score in your analysis. A beautiful molecule is useless if it cannot be synthesized. Use --sa-threshold 5.0 or 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.