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exmoset-0.1.0
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
Automating the generation of human readable descriptions of arbitrary subsets of molecular space.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | exmoset-0.1.0 |
| نام | exmoset |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Adam Mater |
| ایمیل نویسنده | adam.mater@anu.edu.au |
| آدرس صفحه اصلی | https://github.com/acmater/exmoset |
| آدرس اینترنتی | https://pypi.org/project/exmoset/ |
| مجوز | - |
# EXplainable MOlecular SETs
Package to automate the identification of molecular similarity given an arbitrary set
of molecules and associated functions to calculate the value of particular properties (label fingerprints).
# Installation
The easiest way to install is using pip following the setup of a new conda environment with rdkit installed (rdkit does not play well with pip).
1. `conda create -n exmoset python=3.8`
2. `conda activate exmoset`
3. `conda install -c conda-forge rdkit`
4. `pip install exmoset`
## API
The `MolSpace` Class handles the analysis of a given molecular set in accordance with the list of fingerprints provided. The molecules can be passed to Molspace in any format, with additional conversions specified by the `mol_converters` argument.
```python
analysis = MolSpace(molecules,
fingerprints = fingerprints,
file="data/QM9_Data.csv",
mol_converters={"rd" : Chem.MolFromSmiles, "smiles" : str},
index_col="SMILES")
```
### Fingerprints
Fingerprints are a standardized way for Molspace to calculate the properties for each molecule it is analysing. Its arguments determine the grammatical structure of the label that will be produced (`property, noun and verb`), and a function to calculate the property (`calculator`) along with what molecular format this function works on (`mol_format`). The grammatical structure of the resulting labels is a work in progress, and may lead to some poor results that require further processing.
```python
def contains_C(mol):
return 1 if C in mol else 0
contains_carbon = Fingerprint(property="Contains C",
verb="contain",
noun="Molecule",
label_type="binary",
calculator=contains_C,
mol_format="smiles")
```
### Molecule Converters
The mol_converters argument provides the means to transform each molecule into alternate representations. The argument is a dictionary with the following structure {Identifier : Function_that_will_convert} that is expanded in the following way:
```python
formats = {key : mol_converters[key](mol) for key in mol_converters.keys()} # Assigns each identifier to its assocaited representation by
self.Molecules.append(Molecule(mol, **formats)) # Unpacks the new formats as kwargs into the Molecule object
```
An example is provided below
```python
mol_converters = {"rd" : Chem.MolFromSmiles, "smiles" : str} # Will convert molecules provided as smiles strings into Chem.rd objects from RDKit and maintain the SMILES in the dataset as strings.
```
## Label Types
### Binary
Binary labels indicate the presence of absence of a particular element, bond type, or molecular feature (such as aromaticity). Simplest to calculate and best behaved with respect to the entropy estimators. Uses a discrete entropy estimator.
### Multiclass
Discrete labels where the value can be any integer. Examples include number of rings, number of atoms, or number of each type of bond. Uses a discrete entropy estimator.
### Continuous
Continuous labels where the value can be any real number. Examples include electronic spatial extent, dipole moment, and free energy. Uses the continuous entropy estimator
## References
The mathematical methods employed in this codebase are based on the following publications:
- Kraskov, A.; Stögbauer, H.; Grassberger, P. Estimating mutual information. Phys. Rev. E 2004, 69, 66138.
- Ross, B. C. Mutual Information between Discrete and Continuous Data Sets. PLOS ONE 2014, 9, 1–5.
Continuous entropy estimation is provided by Paul Broderson's entropy estimators package (https://github.com/paulbrodersen/entropy_estimators).
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8, <4 | Python |
نحوه نصب
نصب پکیج whl exmoset-0.1.0:
pip install exmoset-0.1.0.whl
نصب پکیج tar.gz exmoset-0.1.0:
pip install exmoset-0.1.0.tar.gz