Signals Notebook: Search Materials using ChemScript python library

You can use the ChemOffice ChemScript library for python to search structures on the SNB API.

ChemScript 22 supports python 3.9 and 3.10.

The following query will search through a Materials library, other query examples can be found on the article "Signals Notebook: Search chemical structures using the API"

{
	"query": {
		"$and": [
			{
				"$match": {
					"field": "assetTypeEid",
					"mode": "keyword",
					"value": "assetType:YOUR_LIBRARY_EID"
				}
			},
			{
				"$chemsearch": {
					"mime": "chemical/x-mdl-molfile",
					"molecule": "YOURMOLFILE",
					"options": "full=no,similar=no"
				}
			},
			{
				"$and": [
					{
						"$match": {
							"field": "isMaterial",
							"value": true
						}
					},
					{
						"$not": [
							{
								"$match": {
									"field": "type",
									"value": "assetType"
								}
							}
						]
					}
				]
			}
		]
	}
}

For the purpose of this example:

• The sdf file will be called structures.sdf
• The query file will be called query.json
• Your API key is added as text on the script headers, it's recommended your API key comes from a secure system variable instead.

The following script iterates through the sdf file, queries for any matches and returns a response:

from ChemScript import *
import requests
import json

baseURL = 'https://tenant.signalsresearch.revvitycloud.com/api/rest/v1.0/'

searchEndpoint = '/entities/search?page%5Boffset%5D=0&page%5Blimit%5D=100&source=SN'

sdf = SDFileReader.OpenFile('structures.sdf')
filetemplate  = open('query.json', 'r')
querybody =filetemplate.read()

headers = {'x-api-key': 'YOUR_KEY',
        'Content-Type': 'application/vnd.api+json',
        'Accept-Encoding': 'gzip, deflate, br'}

structure = sdf.ReadNext()
while (structure != None):
    mol = structure.WriteData('chemical/x-mdl-molfile')
    mol = str(mol,'utf-8')
    body = json.loads(querybody)
    body['query']['$and'][1]['$chemsearch']['molecule'] = mol #insert the molfile into the query
    response = requests.post(url=baseURL+searchEndpoint, json=body, headers = headers)
    responsebody = json.loads(response.content)
    print(responsebody['meta']) #do what you need to do with the response

    structure = sdf.ReadNext()

This script will only return the first 100 results due to the limit on the endpoint, a paging method needs to be implemented if more than 100 results need to processed per request.