TaproomDataSet

class openff.evaluator.datasets.taproom.TaproomDataSet(host_codes: Optional[List[str]] = None, guest_codes: Optional[List[str]] = None, default_ionic_strength: Optional[Quantity] = 150 * unit.millimolar, negative_buffer_ion: str = '[Cl-]', positive_buffer_ion: str = '[Na+]', attach_apr_meta_data: bool = True)[source]

A dataset of host-guest binding affinity measurements which sources its data from the taproom package.

The loaded HostGuestBindingAffinity properties will also be optionally (enabled by default) initialized with the metadata required by the APR estimation workflow.

__init__(host_codes: Optional[List[str]] = None, guest_codes: Optional[List[str]] = None, default_ionic_strength: Optional[Quantity] = 150 * unit.millimolar, negative_buffer_ion: str = '[Cl-]', positive_buffer_ion: str = '[Na+]', attach_apr_meta_data: bool = True)[source]
Parameters

  • host_codes – The three letter codes of the host molecules to load from taproom If no list is provided, all hosts will be loaded.

  • guest_codes – The three letter codes of the guest molecules to load from taproom. If no list is provided, all guests will be loaded.

  • default_ionic_strength – The default ionic strength to use for measurements. The value specified in taproom will be ignored and this value used instead. If no value is provided, no buffer will be included.

  • negative_buffer_ion – The SMILES pattern of the negative buffer ion to use. The value specified in taproom will be ignored and this value used instead.

  • positive_buffer_ion – The SMILES pattern of the positive buffer ion to use. The value specified in taproom will be ignored and this value used instead.

  • attach_apr_meta_data – Whether to add the metadata required for an APR based calculation using the paprika based workflow.

Methods

__init__([host_codes, guest_codes, ...])

param host_codes

The three letter codes of the host molecules to load from taproom

add_properties(*physical_properties[, validate])

Adds a physical property to the data set.

from_json(file_path)

Create this object from a JSON file.

from_pandas(data_frame)

Constructs a data set object from a pandas DataFrame object.

json([file_path, format])

Creates a JSON representation of this class.

merge(data_set[, validate])

Merge another data set into the current one.

parse_json(string_contents)

Parses a typed json string into the corresponding class structure.

properties_by_substance(substance)

A generator which may be used to loop over all of the properties which were measured for a particular substance.

properties_by_type(property_type)

A generator which may be used to loop over all of properties of a particular type, e.g.

to_pandas()

Converts a PhysicalPropertyDataSet to a pandas.DataFrame object with columns of

validate()

Checks to ensure that all properties within the set are valid physical property object.

Attributes

properties

A list of all of the properties within this set.

property_types

The types of property within this data set.

sources

The sources from which the properties in this data set were gathered.

substances

The substances for which the properties in this data set were collected for.
add_properties(*physical_properties, validate=True)

Adds a physical property to the data set.

Parameters

  • physical_properties (PhysicalProperty) – The physical property to add.

  • validate (bool) – Whether to validate the properties before adding them to the set.

classmethod from_json(file_path)

Create this object from a JSON file.

Parameters

file_path (str) – The path to load the JSON from.

Returns

The parsed class.

Return type

cls

classmethod from_pandas(data_frame: DataFrame) → PhysicalPropertyDataSet

Constructs a data set object from a pandas DataFrame object.

Notes

  • All physical properties are assumed to be source from experimental measurements.

  • Currently this method onlu supports data frames containing properties which are built-in to the framework (e.g. Density).

  • This method assumes the data frame has a structure identical to that produced by the PhysicalPropertyDataSet.to_pandas function.

Parameters

data_frame – The data frame to construct the data set from.

Return type

The constructed data set.

json(file_path=None, format=False)

Creates a JSON representation of this class.

Parameters

  • file_path (str, optional) – The (optional) file path to save the JSON file to.

  • format (bool) – Whether to format the JSON or not.

Returns

The JSON representation of this class.

Return type

str

merge(data_set, validate=True)

Merge another data set into the current one.

Parameters

  • data_set (PhysicalPropertyDataSet) – The secondary data set to merge into this one.

  • validate (bool) – Whether to validate the other data set before merging.

classmethod parse_json(string_contents)

Parses a typed json string into the corresponding class structure.

Parameters

string_contents (str or bytes) – The typed json string.

Returns

The parsed class.

Return type

Any

property properties

A list of all of the properties within this set.

Type

tuple of PhysicalProperty

properties_by_substance(substance)

A generator which may be used to loop over all of the properties which were measured for a particular substance.

Parameters

substance (Substance) – The substance of interest.

Return type

generator of PhysicalProperty

properties_by_type(property_type)

A generator which may be used to loop over all of properties of a particular type, e.g. all “Density” properties.

Parameters

property_type (str or type of PhysicalProperty) – The type of property of interest. This may either be the string class name of the property or the class type.

Return type

generator of PhysicalProperty

property property_types

The types of property within this data set.

Type

set of str

property sources

The sources from which the properties in this data set were gathered.

Type

set of Source

property substances

The substances for which the properties in this data set were collected for.

Type

set of Substance

to_pandas()

Converts a PhysicalPropertyDataSet to a pandas.DataFrame object with columns of

  • ‘Id’

  • ‘Temperature (K)’

  • ‘Pressure (kPa)’

  • ‘Phase’

  • ‘N Components’

  • ‘Component 1’

  • ‘Role 1’

  • ‘Mole Fraction 1’

  • ‘Exact Amount 1’

  • …

  • ‘Component N’

  • ‘Role N’

  • ‘Mole Fraction N’

  • ‘Exact Amount N’

  • ‘<Property 1> Value (<default unit>)’

  • ‘<Property 1> Uncertainty / (<default unit>)’

  • …

  • ‘<Property N> Value / (<default unit>)’

  • ‘<Property N> Uncertainty / (<default unit>)’

  • ‘Source’

where ‘Component X’ is a column containing the smiles representation of component X.

Returns

The create data frame.

Return type

DataFrame

validate()

Checks to ensure that all properties within the set are valid physical property object.