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Log-solubility (logP)

Log-solubility (logP)#

Type of objective function: discrete Environment to run this objective function: poli chem

About#

This objective function returns the partition coefficient of a solute between octanol and water (known as logP) using RDKit [RDKit, 2006]. You can specify whether you are measuring the logP of a SMILES string, or a SELFIES.

Prerequisites#

None. This black box should work out-of-the-box.

How to run#

import numpy as np
from poli.objective_repository import LogPProblemFactory, LogPBlackBox

# Creating the black box
f = LogPBlackBox(string_representation="SMILES")

# Creating a problem
problem = LogPProblemFactory().create(string_representation="SMILES")
f, x0 = problem.black_box, problem.x0

# Example input: a single carbon
x = np.array(["C"]).reshape(1, -1)

# Querying:
y = f(x)
print(y)  # Should be close to 0.6361

How to cite#

If you use this black box, we expect you to cite the following resources:

[1] RDKit community. (2006). RDKit: Open-source cheminformatics. GitHub. Available at: rdkit/rdkit. Accessed on 12th of April, 2024.

[2] González-Duque, M., Bartels, S., & Michael, R. (2024). poli: a libary of discrete sequence objectives [Computer software]. MachineLearningLifeScience/poli

@misc{rdkit,
  author = {RDKit},
  title = {RDKit: Open-source cheminformatics},
  year = {2006},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/rdkit/rdkit}}
}


@software{Gonzalez-Duque:poli:2024,
author = {González-Duque, Miguel and Bartels, Simon and Michael, Richard},
month = jan,
title = {{poli: a libary of discrete sequence objectives}},
url = {https://github.com/MachineLearningLifeScience/poli},
version = {0.0.1},
year = {2024}
}
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