BBB example for LIME

.github/workflows/paper.yml

@@ -13,10 +13,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
-        python-version: "3.8"
+        python-version: "3.10"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.pre-commit-config.yaml

@@ -1,12 +1,12 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.2.3
+    rev: v4.5.0
     hooks:
-      - id: trailing-whitespace
       - id: check-yaml
       - id: end-of-file-fixer
+      - id: trailing-whitespace
       - id: mixed-line-ending
-  - repo: https://github.com/psf/black
+  - repo: https://github.com/psf/black-pre-commit-mirror
     rev: "23.11.0"
     hooks:
       - id: black

exmol/exmol.py

@@ -27,9 +27,10 @@
 from rdkit.Chem.Draw import MolToImage as mol2img, DrawMorganBit  # type: ignore
 from rdkit.Chem import rdchem  # type: ignore
 from rdkit.DataStructs.cDataStructs import BulkTanimotoSimilarity, TanimotoSimilarity  # type: ignore
-import langchain.llms as llms
-import langchain.prompts as prompts
 
+from openai import OpenAI
+
+client = OpenAI()
 from . import stoned
 from .plot_utils import _mol_images, _image_scatter, _bit2atoms
 from .data import *
@@ -392,6 +393,7 @@ def _check_alphabet_consistency(
     alphabet_symbols = _alphabet_to_elements(set(alphabet_symbols))
     # find all elements in smiles (Upper alpha or upper alpha followed by lower alpha)
     smiles_symbols = set(re.findall(r"[A-Z][a-z]?", smiles))
+
     if check and not smiles_symbols.issubset(alphabet_symbols):
         # show which symbols are not in alphabet
         raise ValueError(
@@ -1410,7 +1412,7 @@ def merge_text_explains(
 def text_explain_generate(
     text_explanations: List[Tuple[str, float]],
     property_name: str,
-    llm: Optional[llms.BaseLLM] = None,
+    llm_model: str = "gpt-4",
     single: bool = True,
 ) -> str:
     """Insert text explanations into template, and generate explanation.
@@ -1430,14 +1432,22 @@ def text_explain_generate(
             for x in text_explanations
         ]
     )
-    prompt_template = prompts.PromptTemplate(
-        input_variables=["property", "text"],
-        template=_single_prompt if single else _multi_prompt,
-    )
+
+    prompt_template = _single_prompt if single else _multi_prompt
     prompt = prompt_template.format(property=property_name, text=text)
-    if llm is None:
-        llm = llms.OpenAI(temperature=0.05)
-    return llm(prompt)
+
+    messages = [
+        {
+            "role": "system",
+            "content": "Your goal is to explain which molecular features are important to its properties based on the given text.",
+        },
+        {"role": "user", "content": prompt},
+    ]
+    response = client.chat.completions.create(
+        model=llm_model, messages=messages, temperature=0.05
+    )
+
+    return response.choices[0].message.content
 
 
 def text_explain(

exmol/stoned/stoned.py

@@ -214,6 +214,7 @@
 from rdkit import Chem  # type: ignore
 from rdkit.Chem import MolFromSmiles as smi2mol  # type: ignore
 from rdkit.Chem import MolToSmiles as mol2smi  # type: ignore
+from rdkit.Chem import MolToRandomSmilesVect # type: ignore
 
 from rdkit.Chem import AllChem  # type: ignore
 from rdkit.DataStructs.cDataStructs import TanimotoSimilarity  # type: ignore
@@ -237,9 +238,10 @@ def randomize_smiles(mol):
     if not mol:
         return None
 
-    return mol2smi(
-        mol, canonical=False, doRandom=True, isomericSmiles=True, kekuleSmiles=True
-    )
+    # return mol2smi(
+    #     mol, canonical=False, doRandom=True, isomericSmiles=True, kekuleSmiles=True
+    # )
+    return MolToRandomSmilesVect(mol, 1, isomericSmiles=True, kekuleSmiles=True, randomSeed=np.random.randint(0,100))[0]
 
 
 def largest_mol(smiles):