mirror of
https://github.com/InternLM/InternBootcamp.git
synced 2026-04-19 12:58:04 +00:00
fb4009b871
- Add random selection of InChI and SMILES strings - Implement relative error-based scoring for logP prediction - Update verification functions to return scores instead of boolean - Refactor InChI and SMILES generation for better randomness
67 lines
2.7 KiB
Python
Executable file
67 lines
2.7 KiB
Python
Executable file
import random
|
|
|
|
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,min_len=5, max_len=25,
|
|
seed=None):
|
|
# super.__init__()
|
|
self.min_len = min_len
|
|
self.max_len = max_len
|
|
# self.SMILESGenerator = SMILESGenerator(min_len=min_len, max_len=max_len, seed=seed)
|
|
|
|
def case_generator(self) -> str:
|
|
"""
|
|
生成一组数字和目标值。
|
|
"""
|
|
self.SMILESGenerator = SMILESGenerator(min_len=self.min_len, max_len=self.max_len, seed=None)
|
|
return self.SMILESGenerator.generate_n_valid_smiles(10)[random.randint(0, 9)]
|
|
|
|
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) -> float:
|
|
"""
|
|
Verify the correction of the solution and return a score between 0 and 1.
|
|
The score is based on the relative error with respect to a maximum relative error of 0.1.
|
|
"""
|
|
mol = Chem.MolFromSmiles(SMILES)
|
|
if mol is None:
|
|
raise ValueError("Invalid SMILES string provided.")
|
|
|
|
true_logp = Crippen.MolLogP(mol)
|
|
solution_float = float(solution)
|
|
|
|
# print('true_logp: ', true_logp, ' solution_float: ', solution_float)
|
|
|
|
# Handle case where true_logp is 0
|
|
if true_logp == 0:
|
|
# If true_logp is 0, we check how close the solution is to 0
|
|
relative_error = abs(solution_float)
|
|
else:
|
|
# Calculate the relative error
|
|
relative_error = abs(true_logp - solution_float) / abs(true_logp)
|
|
|
|
# Define the maximum allowed relative error
|
|
max_relative_error = 0.1
|
|
|
|
# Calculate the score based on the relative error
|
|
if relative_error >= max_relative_error:
|
|
return 0.0 # Error is too large, score is 0
|
|
else:
|
|
# Linear interpolation: score decreases linearly from 1 to 0 as error goes from 0 to max_relative_error
|
|
return 1.0
|
|
return 1 - (relative_error / max_relative_error) * 0.5 ## For RL
|