schrodinger.application.transforms.autosar.util module

class schrodinger.application.transforms.autosar.util.Cutoff(min: float, max: float)

Bases: object

Represent an inclusive min and inclusive max cutoff for a property

min: float
max: float
__init__(min: float, max: float) → None
class schrodinger.application.transforms.autosar.util.RGroup(smiles: str, properties: dict[str, float] = <factory>)

Bases: object

An RGroup and it associated properties

When accessing a property using the index operator (e.g., rgroup[key]), it will return the value of the property from the underlying properties dictionary.

NOTE: This dataclass is intended to be used with SMILES that encode the attachment

point and R-group index within SMILES itself. For example, a SMILES of “CC[*:3]” defines both the attachment point and that it is RGroup 3.

NOTE: We assume RGroups with the exact same SMILES are equivalent and that their

properties are also equivalent. This allows us to hash RGroup instances using just the SMILES string.

Parameters:

  • smiles – the SMILES string of the RGroup

  • properties – a dictionary mapping property names to their values

smiles: str
properties: dict[str, float]
__init__(smiles: str, properties: dict[str, float] = <factory>) → None
schrodinger.application.transforms.autosar.util.to_csv_str(row: dict[str, Any], fieldnames: list[str]) → str

Convert a dictionary to a string representing a single row in a CSV file.

Parameters:

  • row – the dictionary to convert to a CSV string

  • fieldnames – the list of fieldnames to use for the CSV output

schrodinger.application.transforms.autosar.util.read_csv(file: str, id_fieldname: str, value_fieldnames: Optional[list[str]] = None) → list[dict[str, str | float]]

Read a CSV file to a list of PropertyDictWithID.

NOTE: Cells in the value_fieldnames columns are converted to floats. If a cell is

empty, it is converted to NaN.

Parameters:

  • file – the path to the CSV file

  • id_fieldname – the column to use as the identifier for each row

  • value_fieldnames – the columns to use as the properties for each row.

schrodinger.application.transforms.autosar.util.read_all_rgroups(input_paths: list[str], smiles_fieldname: str, value_fieldnames: list[str]) → list[list[RGroup]]

Read all RGroups from the given input CSV paths.

Parameters:

  • input_paths – a list of paths to the CSV files

  • smiles_fieldname – the column to use as the SMILES for each RGroup

  • value_fieldnames – the columns to use as the properties for each RGroup

schrodinger.application.transforms.autosar.util.read_rgroups(input_path: str, smiles_fieldname: str, value_fieldnames: list[str]) → list[RGroup]

Read RGroups from a single CSV file.

Parameters:

  • input_path – the path to the CSV file

  • smiles_fieldname – the column to use as the SMILES for each RGroup

  • value_fieldnames – the columns to use as the properties for each RGroup

schrodinger.application.transforms.autosar.util.get_properties_with_negative_values(all_rgroups: list[list[RGroup]], properties_to_check: list[str]) → list[str]

Over all RGroups determine which properties have at least one negative value.

Parameters:

  • all_rgroups – a list of lists of RGroups

  • properties_to_check – the properties to check for negative values

schrodinger.application.transforms.autosar.util.normalize_cutoffs(cutoffs: dict[str, Cutoff], ref_smiles: str, ref_properties: dict[str, float]) → dict[str, Cutoff]

Adjust the cutoffs to be relative to the reference properties.

Parameters:

  • cutoffs – the cutoffs to adjust

  • ref_smiles – the SMILES of the reference molecule

  • ref_properties – the reference properties