adding MR and LogP Prediction tasks

internbootcamp/bootcamp/ChemStructure2Property/SMILES2logPBootCamp.py

@@ -0,0 +1,43 @@
+from internbootcamp.bootcamp.base import Basebootcamp
+from internbootcamp.libs.chemStructure2Property.ChemStructureGenerator import SMILESGenerator
+from .utils import last_boxed_only_string, remove_boxed
+from rdkit import Chem
+from rdkit.Chem import Crippen
+
+from .InChI2logPBootCamp import InChI2logPbootcamp
+
+class SMILES2logPBootCamp(InChI2logPbootcamp):
+    def __init__(self, num_numbers=4, min_len=5, max_len=25, 
+                 seed=None):
+        # super.__init__()
+        self.num_numbers = num_numbers
+        self.SMILESGenerator = SMILESGenerator(min_len=5, max_len=25, seed=None)
+        
+    def case_generator(self) -> str:
+        """
+        生成一组数字和目标值。
+        """
+        return self.SMILESGenerator.generate_n_valid_smiles(1)[0]
+
+    def prompt_func(self,  SMILES) -> str:
+
+        instruction = f"Given the  SMILES, determine the lipophilicity (logP) value of the material. The  SMILES is: {SMILES}"
+        instruction_following = """Let's think step by step and output the final answer within \\boxed{}.The final answer should be one float number. For example "Final Answer: \\boxed{afloat}"."""
+        
+        prompt = instruction + '\n' + instruction_following
+        return prompt
+
+        
+    @classmethod 
+    def _verify_correction(cls, solution, SMILES)->bool:
+        """
+        Verify the correction of the solution.
+        """ 
+        mol = Chem.MolFromSmiles(SMILES)
+        true_logp = Crippen.MolLogP(mol)
+        print(f"Comparing pred: {solution}, ground_truth: {true_logp}")
+        return abs(true_logp - float(solution)) <= 0.01  # maybe mse or mae better?
+        
+
+
+