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18f47e0a3a
- Reduce the number of generated InChIs and SMILES from 10 to 1 - Remove random selection, always return the first generated structure - Comment out debug prints and unused code
67 lines
2.7 KiB
Python
Executable file
67 lines
2.7 KiB
Python
Executable file
import random
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from internbootcamp.bootcamp.base import Basebootcamp
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from internbootcamp.libs.chemStructure2Property.ChemStructureGenerator import SMILESGenerator
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from .utils import last_boxed_only_string, remove_boxed
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from rdkit import Chem
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from rdkit.Chem import Crippen
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from .InChI2logPBootCamp import InChI2logPbootcamp
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class SMILES2logPbootcamp(InChI2logPbootcamp):
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def __init__(self,min_len=5, max_len=25,
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seed=None):
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# super.__init__()
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self.min_len = min_len
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self.max_len = max_len
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# self.SMILESGenerator = SMILESGenerator(min_len=min_len, max_len=max_len, seed=seed)
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def case_generator(self) -> str:
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"""
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生成一组数字和目标值。
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"""
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self.SMILESGenerator = SMILESGenerator(min_len=self.min_len, max_len=self.max_len, seed=None)
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return self.SMILESGenerator.generate_n_valid_smiles(1)[0]
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def prompt_func(self, SMILES) -> str:
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instruction = f"Given the SMILES, determine the lipophilicity (logP) value of the material. The SMILES is: {SMILES}"
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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}"."""
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prompt = instruction + '\n' + instruction_following
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return prompt
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@classmethod
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def _verify_correction(cls, solution, SMILES) -> float:
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"""
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Verify the correction of the solution and return a score between 0 and 1.
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The score is based on the relative error with respect to a maximum relative error of 0.1.
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"""
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mol = Chem.MolFromSmiles(SMILES)
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if mol is None:
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raise ValueError("Invalid SMILES string provided.")
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true_logp = Crippen.MolLogP(mol)
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solution_float = float(solution)
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# print('true_logp: ', true_logp, ' solution_float: ', solution_float)
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# Handle case where true_logp is 0
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if true_logp == 0:
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# If true_logp is 0, we check how close the solution is to 0
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relative_error = abs(solution_float)
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else:
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# Calculate the relative error
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relative_error = abs(true_logp - solution_float) / abs(true_logp)
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# Define the maximum allowed relative error
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max_relative_error = 0.1
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# Calculate the score based on the relative error
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if relative_error >= max_relative_error:
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return 0.0 # Error is too large, score is 0
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else:
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# Linear interpolation: score decreases linearly from 1 to 0 as error goes from 0 to max_relative_error
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# return 1.0
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return 1 - (relative_error / max_relative_error) * 0.5 ## For RL
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