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import logging |
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from dataclasses import dataclass |
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from typing import Sequence, Set, Optional |
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import re |
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from pocketutils.core.dot_dict import NestedDotDict |
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from mandos.model.chembl_api import ChemblApi |
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from mandos.model.chembl_support import ChemblCompound |
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from mandos.model.chembl_support.chembl_targets import ChemblTarget |
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from mandos.model.defaults import Defaults |
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from mandos.model.taxonomy import Taxonomy |
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from mandos.search.chembl._protein_search import ProteinHit, ProteinSearch |
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from mandos.search.chembl.target_traversal import ( |
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TargetTraversalStrategy, |
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TargetTraversalStrategies, |
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) |
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logger = logging.getLogger("mandos") |
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@dataclass(frozen=True, order=True, repr=True) |
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class BindingHit(ProteinHit): |
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""" |
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An "activity" hit for a compound. |
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""" |
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taxon_id: int |
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taxon_name: str |
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pchembl: float |
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std_type: str |
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src_id: str |
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exact_target_id: str |
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@property |
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def predicate(self) -> str: |
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return "activity" |
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class BindingSearch(ProteinSearch[BindingHit]): |
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""" |
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Search for ``activity`` of type "B". |
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""" |
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def __init__( |
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self, |
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chembl_api: ChemblApi, |
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tax: Taxonomy, |
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traversal_strategy: str, |
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allowed_target_types: Set[str], |
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min_confidence_score: Optional[int], |
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allowed_relations: Set[str], |
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min_pchembl: float, |
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banned_flags: Set[str], |
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): |
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super().__init__(chembl_api, tax, traversal_strategy) |
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self.allowed_target_types = allowed_target_types |
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self.min_confidence_score = min_confidence_score |
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self.allowed_relations = allowed_relations |
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self.min_pchembl = min_pchembl |
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self.banned_flags = banned_flags |
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@property |
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def default_traversal_strategy(self) -> TargetTraversalStrategy: |
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return TargetTraversalStrategies.strategy0(self.api) |
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def query(self, parent_form: ChemblCompound) -> Sequence[NestedDotDict]: |
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def set_to_regex(values) -> str: |
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return "(" + "|".join([f"(?:{re.escape(v)})" for v in values]) + ")" |
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filters = dict( |
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parent_molecule_chembl_id=parent_form.chid, |
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assay_type="B", |
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standard_relation__iregex=set_to_regex(self.allowed_relations), |
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pchembl_value__isnull=False, |
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target_organism__isnull=None if self.taxonomy is None else False, |
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) |
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# I'd rather not figure out how the API interprets None, so remove them |
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filters = {k: v for k, v in filters.items() if v is not None} |
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return list(self.api.activity.filter(**filters)) |
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def should_include( |
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self, lookup: str, compound: ChemblCompound, data: NestedDotDict, target: ChemblTarget |
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) -> bool: |
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if ( |
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( |
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data.get_as("data_validity_comment", lambda s: s.lower()) |
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in {s.lower() for s in self.banned_flags} |
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) |
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or (data.req_as("standard_relation", str) not in self.allowed_relations) |
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or (data.req_as("assay_type", str) != "B") |
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or ( |
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self.taxonomy is not None and data.get_as("target_tax_id", int) not in self.taxonomy |
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) |
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or (data.get("pchembl_value") is None) |
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or (data.req_as("pchembl_value", float) < self.min_pchembl) |
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): |
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return False |
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if data.get("data_validity_comment") is not None: |
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logger.warning( |
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f"Activity annotation for {lookup} has flag '{data.get('data_validity_comment')} (ok)" |
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) |
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# The `target_organism` doesn't always match the `assay_organism` |
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# Ex: see assay CHEMBL823141 / document CHEMBL1135642 for homo sapiens in xenopus laevis |
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# However, it's often something like yeast expressing a human / mouse / etc receptor |
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# So there's no need to filter by it |
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assay = self.api.assay.get(data.req_as("assay_chembl_id", str)) |
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confidence_score = assay.get("confidence_score") |
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if target.type.name.lower() not in {s.lower() for s in self.allowed_target_types}: |
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logger.warning(f"Excluding {target} with type {target.type}") |
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return False |
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if self.min_confidence_score is not None: |
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if confidence_score is None or confidence_score < self.min_confidence_score: |
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return False |
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# Some of these are non-protein types |
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# And if it's unknown, we don't know what to do with it |
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return True |
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def to_hit( |
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self, lookup: str, compound: ChemblCompound, data: NestedDotDict, target: ChemblTarget |
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) -> Sequence[BindingHit]: |
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# these must match the constructor of the Hit, |
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# EXCEPT for object_id and object_name, which come from traversal |
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x = self._extract(lookup, compound, data) |
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return [BindingHit(**x, object_id=target.chembl, object_name=target.name)] |
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def _extract(self, lookup: str, compound: ChemblCompound, data: NestedDotDict) -> NestedDotDict: |
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# we know these exist from the query |
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if self.taxonomy is None: |
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tax = None |
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else: |
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organism = data.req_as("target_organism", str) |
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tax_id = data.req_as("target_tax_id", int) |
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tax = self.taxonomy.req(tax_id) |
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if organism != tax.name: |
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logger.warning(f"Target organism {organism} is not {tax.name}") |
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return NestedDotDict( |
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dict( |
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record_id=data.req_as("activity_id", str), |
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compound_id=compound.chid, |
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inchikey=compound.inchikey, |
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compound_name=compound.name, |
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compound_lookup=lookup, |
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taxon_id=None if tax is None else tax.id, |
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taxon_name=None if tax is None else tax.name, |
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pchembl=data.req_as("pchembl_value", float), |
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std_type=data.req_as("standard_type", str), |
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src_id=data.req_as("src_id", str), |
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exact_target_id=data.req_as("target_chembl_id", str), |
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) |
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) |
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__all__ = ["BindingHit", "BindingSearch"] |
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dmyersturnbull /
mandos
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