adding MR and LogP Prediction tasks

internbootcamp/bootcamp/ChemStructure2Property/InChI2logPBootCamp.py

@@ -0,0 +1,56 @@
+from internbootcamp.bootcamp.base import Basebootcamp
+from internbootcamp.libs.chemStructure2Property.ChemStructureGenerator import InChIGenerator
+from .utils import last_boxed_only_string, remove_boxed
+from rdkit import Chem
+from rdkit.Chem import Crippen
+
+
+class InChI2logPbootcamp(Basebootcamp):
+    def __init__(self, num_numbers=4, max_atoms=15, min_atoms=3, elements=None, seed=None):
+        # super.__init__()
+        self.num_numbers = num_numbers
+        self.InChIGenerator = InChIGenerator(max_atoms=max_atoms, min_atoms=min_atoms, elements=elements, seed=seed)
+        
+    def case_generator(self) -> str:
+        """
+        生成一组数字和目标值。
+        """
+        return self.InChIGenerator.generate_n_valid_inchi(1)[0]
+
+    def prompt_func(self, InChI) -> str:
+
+        instruction = f"Given the InChI, determine the lipophilicity (logP) value of the material. The InChI is: {InChI}"
+        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
+    
+    @staticmethod
+    def extract_output(output):
+        """
+        Extract the output from the solution.
+        
+        Args:
+            output: Model output to be processed.
+        
+        Returns:
+            The processed output.
+        """
+        output = last_boxed_only_string(output)
+        if output is None:
+            return None
+        return remove_boxed(output)
+        
+    @classmethod 
+    def _verify_correction(cls, solution, InChI)->bool:
+        """
+        Verify the correction of the solution.
+        """ 
+        mol = Chem.MolFromInchi(InChI)
+        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?
+        
+
+
+