API Documentation

{
  "features": [
    {
      "type": "HBD",
      "x": 1.1,
      "y": 0.6,
      "z": 1.9,
      "radius": 2.0,
      "optional": false
    },
    {
      "type": "HBA",
      "x": 2.7,
      "y": 1.6,
      "z": 3.3,
      "radius": 2.0,
      "optional": false
    },
    {
      "type": "aromatic",
      "x": -1.3,
      "y": 2.0,
      "z": 2.6,
      "radius": 2.0,
      "optional": false
    }
  ]
}

curl -X POST "http://localhost:8000/api/search/pharmacophore?max_results=100&similarity_threshold=0.7" \
  -H "Content-Type: application/json" \
  -d '{
    "features": [
      {"type": "HBD", "x": 1.1, "y": 0.6, "z": 1.9, "radius": 2.0, "optional": false},
      {"type": "HBA", "x": 2.7, "y": 1.6, "z": 3.3, "radius": 2.0, "optional": false},
      {"type": "aromatic", "x": -1.3, "y": 2.0, "z": 2.6, "radius": 2.0, "optional": false}
    ]
  }'

import requests
import json

# Define pharmacophore query
query = {
    "features": [
        {"type": "HBD", "x": 1.1, "y": 0.6, "z": 1.9, "radius": 2.0, "optional": False},
        {"type": "HBA", "x": 2.7, "y": 1.6, "z": 3.3, "radius": 2.0, "optional": False},
        {"type": "aromatic", "x": -1.3, "y": 2.0, "z": 2.6, "radius": 2.0, "optional": False}
    ]
}

# Search database
response = requests.post(
    "http://localhost:8000/api/search/pharmacophore",
    params={"max_results": 100, "similarity_threshold": 0.7},
    json=query
)

# Process results
results = response.json()
for hit in results["results"]:
    print(f"{hit['molecule']['name']}: {hit['match_score']:.1%} match")
    print(f"  SMILES: {hit['molecule']['smiles']}")
    print(f"  Matched Features: {hit['matched_features']}/{hit['total_features']}")
    print()

{
  "query": {
    "features": [...],
    "max_results": 100,
    "similarity_threshold": 0.7
  },
  "results": [
    {
      "molecule": {
        "id": "mol_001",
        "name": "Imatinib",
        "smiles": "CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CC=CC=N5",
        "source": "DrugBank",
        "molecular_weight": 493.6,
        "logp": 3.45,
        "hbd_count": 2,
        "hba_count": 7
      },
      "match_score": 1.0,
      "matched_features": 4,
      "total_features": 4,
      "feature_matches": [
        {"query_index": 0, "type": "HBD", "distance": 0.15},
        {"query_index": 1, "type": "HBA", "distance": 0.23},
        {"query_index": 2, "type": "aromatic", "distance": 0.18},
        {"query_index": 3, "type": "hydrophobic", "distance": 0.45}
      ]
    }
  ],
  "total_results": 23,
  "search_time_ms": 145
}

import requests
from typing import List, Dict
import pandas as pd

class PharmacophoreScreener:
    """High-throughput pharmacophore screening client"""

    def __init__(self, base_url: str = "http://localhost:8000"):
        self.base_url = base_url

    def screen_library(self, queries: List[Dict], max_results: int = 1000):
        """Screen multiple pharmacophore queries"""
        results = []

        for i, query in enumerate(queries):
            print(f"Screening query {i+1}/{len(queries)}...")

            response = requests.post(
                f"{self.base_url}/api/search/pharmacophore",
                params={
                    "max_results": max_results,
                    "similarity_threshold": query.get("threshold", 0.7)
                },
                json={"features": query["features"]}
            )

            if response.status_code == 200:
                data = response.json()
                results.append({
                    "query_id": query.get("id", f"query_{i}"),
                    "hits": len(data["results"]),
                    "top_hit": data["results"][0] if data["results"] else None,
                    "search_time_ms": data["search_time_ms"]
                })

        return pd.DataFrame(results)

    def export_results(self, results_df: pd.DataFrame, filename: str):
        """Export screening results to CSV"""
        results_df.to_csv(filename, index=False)
        print(f"Results exported to {filename}")

# Example usage
screener = PharmacophoreScreener()

# Define multiple queries for screening
queries = [
    {
        "id": "kinase_query_1",
        "features": [
            {"type": "HBD", "x": 1.1, "y": 0.6, "z": 1.9, "radius": 2.0, "optional": False},
            {"type": "HBA", "x": 2.7, "y": 1.6, "z": 3.3, "radius": 2.0, "optional": False}
        ],
        "threshold": 0.7
    },
    {
        "id": "gpcr_query_1",
        "features": [
            {"type": "positive", "x": 4.8, "y": -1.6, "z": -2.7, "radius": 2.0, "optional": False},
            {"type": "aromatic", "x": -1.4, "y": 2.5, "z": 2.7, "radius": 2.0, "optional": False}
        ],
        "threshold": 0.65
    }
]

# Run screening campaign
results = screener.screen_library(queries, max_results=500)
screener.export_results(results, "screening_campaign_results.csv")

# Analyze results
print(f"\nTotal queries screened: {len(results)}")
print(f"Total hits found: {results['hits'].sum()}")
print(f"Average search time: {results['search_time_ms'].mean():.1f} ms")

import requests

# Get molecule with calculated properties
molecule_id = "mol_001"
response = requests.get(f"http://localhost:8000/api/molecules/{molecule_id}")

molecule = response.json()
print(f"Molecule: {molecule['name']}")
print(f"SMILES: {molecule['smiles']}")
print(f"\nProperties:")
print(f"  Molecular Weight: {molecule['properties']['molecular_weight']:.2f}")
print(f"  LogP: {molecule['properties']['logp']:.2f}")
print(f"  TPSA: {molecule['properties']['tpsa']:.2f}")
print(f"  HBD: {molecule['properties']['hbd_count']}")
print(f"  HBA: {molecule['properties']['hba_count']}")
print(f"  Rotatable Bonds: {molecule['properties']['rotatable_bonds']}")
print(f"\nLipinski's Rule of Five: {'PASS' if molecule['properties']['lipinski_violations'] == 0 else 'FAIL'}")

import requests

# Convert SMILES to 3D MOL format
smiles = "CC(C)Cc1ccc(C(C)C(=O)O)cc1"  # Ibuprofen
response = requests.get(
    "http://localhost:8000/api/molecules/mol-format",
    params={"smiles": smiles}
)

mol_block = response.json()["mol_format"]

# Save to file for visualization
with open("molecule_3d.mol", "w") as f:
    f.write(mol_block)

print(f"3D structure saved to molecule_3d.mol")
print(f"MOL block size: {len(mol_block)} characters")