AgentSkillsCN

structure-search

基于结构的相似性搜索和骨架分析用于药物发现。适用于先导跳跃、骨架变形和化学空间探索。 关键词:相似性搜索、骨架跳跃、化学空间、指纹、Tanimoto

SKILL.md
--- frontmatter
name: structure-search
description: |
  Structure-based similarity search and scaffold analysis for drug discovery.
  Use for lead hopping, scaffold morphing, and chemical space exploration.

  Keywords: similarity search, scaffold hopping, chemical space, fingerprint, Tanimoto
category: Computational Chemistry
tags: [structure, similarity, fingerprint, scaffold, chemical-space]
version: 1.0.0
author: Drug Discovery Team
dependencies:
  - rdkit
  - chembl
  - pubchem

Structure Search Skill

Structure-based similarity search and scaffold analysis for drug discovery.

Quick Start

code
/structure --query "CC1=CC=C(C=C1)CNC" --threshold 0.7
/scaffold-hop --input compound.sdf --scaffold-type murcko
/similar-compounds --chembl "CHEMBL210" --limit 20

Similarity Methods

Fingerprint-Based

FingerprintSizeBest UseSpeed
Morgan2048General purposeFast
MACCS166General purposeVery fast
RDKit2048Structural featuresFast
Atom pair2048SubstructureMedium
Topological torsion2048ConformationsMedium

Similarity Coefficients

CoefficientRangeProperties
Tanimoto0-1Most common, bounded
Dice0-1Similar to Tanimoto
Cosine0-1Vector-based
Tversky0-1Asymmetric

Scaffold Analysis

Scaffold Types

TypeDefinitionUse Case
MurckoCore ring systemGeneral
Bemis-MurckoRings + linkersDrug-like
RECAPRings + functional groupsMedicinal chemistry
GraphOnly topologyVery generic

Scaffold Hopping

Strategies:

  1. Ring replacement: Bioisosteric substitution
  2. Ring opening/closing: Modify topology
  3. Linker modification: Change connectivity
  4. Heteroatom swap: N→O→S→C

Output Structure

markdown
# Structure Search Results

## Query Compound
**SMILES**: CC1=CC=C(C=C1)CNC
**Name**: Erlotinib
**Scaffold**: c1ccc(cc1)CNCC

## Similar Compounds (Tanimoto ≥ 0.7)

| Rank | ID | Name | Similarity | Scaffold Match |
|------|----|-----|------------|----------------|
| 1 | CHEMBL210 | Erlotinib | 1.00 | Yes |
| 2 | CHEMBL214 | Gefitinib | 0.89 | Yes |
| 3 | CHEMBL617 | Afatinib | 0.82 | Yes |
| 4 | CHEMBL12345 | Novel analog | 0.76 | No |
| 5 | CHEMBL98765 | Lead compound | 0.72 | Yes |

## Scaffold Analysis

### Murcko Scaffold

Query: c1ccc(cc1)CNCC (Quinazoline core)

code

### Known Compounds with This Scaffold

| Compound | Class | Status |
|----------|-------|--------|
| Erlotinib | 1st-gen TKI | Approved |
| Gefitinib | 1st-gen TKI | Approved |
| Afatinib | 2nd-gen TKI | Approved |
| Dacomitinib | 2nd-gen TKI | Approved |
| Osimertinib | 3rd-gen TKI | Approved |

### Scaffold Frequency

| Scaffold | ChEMBL Count | Use |
|----------|--------------|-----|
| Quinazoline | 2,456 | Kinase inhibitors |
| Pyrimidine | 3,789 | Various targets |
| Pyrrolopyrimidine | 456 | Selective kinases |

## Scaffold Hopping Opportunities

### Ring Replacements

| Original | Bioisostere | Rationale |
|----------|-------------|-----------|
| Benzene | Pyridine | Add H-bond acceptor |
| Benzene | Thiophene | Slightly larger, polarizable |
| Pyridine | Pyrimidine | Add H-bond acceptor |
| Phenyl | Cyclohexyl | Remove aromaticity |

### Novel Scaffolds

**Identified 3 novel scaffolds** with similar topology:

1. **Indazole core**: 3 compounds
2. **Pyrrolopyrimidine**: 5 compounds
3. **Imidazopyridazine**: 2 compounds

## Property Comparison

| Property | Query | Mean (Similar) | Range |
|----------|-------|----------------|-------|
| MW | 393 | 420 | 350-480 |
| LogP | 3.2 | 3.5 | 2.8-4.2 |
| HBD | 1 | 1.2 | 1-2 |
| HBA | 3 | 3.5 | 2-5 |
| PSA | 72 | 78 | 65-95 |

## Recommendations

1. **Explore indazole compounds**: Novel scaffold, good properties
2. **Monitor pyrrolopyrimidines**: Emerging scaffold
3. **Consider scaffold hopping**: If IP crowded

Similarity Thresholds

ApplicationTanimotoInterpretation
Identical1.0Same compound
Very similar0.9-1.0Same analog series
Similar0.7-0.9Same scaffold
Related0.5-0.7Similar structure
Distant0.3-0.5Some similarity
Unrelated<0.3Different chemotypes

Running Scripts

bash
# Similarity search
python scripts/structure_search.py --query "SMILES" --threshold 0.7

# Scaffold analysis
python scripts/scaffold_analysis.py --input compounds.sdf --type murcko

# Scaffold hopping
python scripts/scaffold_hop.py --input compound.sdf --output hops.sdf

# Chemical space mapping
python scripts/chemical_space.py --library compounds.sdf --pca

Requirements

bash
pip install rdkit pandas numpy scikit-learn

# Optional for visualization
pip install plotly seaborn matplotlib

Reference

Best Practices

  1. Use appropriate thresholds: 0.7 for similar compounds
  2. Consider scaffold: Different scaffold may have similar activity
  3. Check properties: Similar doesn't mean drug-like
  4. Validate experimentally: In-silico similarity needs confirmation
  5. Use multiple methods: Fingerprints + alignment for full picture

Common Pitfalls

PitfallSolution
High similarity ≠ same activityCheck bioactivity
Ignoring stereochemistryUse isomeric SMILES
Fingerprint biasTry multiple fingerprint types
Scaffold blindnessExplicit scaffold analysis
Over-clusteringAppropriate threshold selection