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Add more structured syntax
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E Dunbar
parent
d330f85c67
commit
66026fe74b
11 changed files with 18956 additions and 4528 deletions
266
test/highlight/data_augmenter.hy
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266
test/highlight/data_augmenter.hy
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#!/usr/bin/env hy
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(import xml.etree.ElementTree :as ET)
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(require hyrule [-> doto meth ncut])
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(import catboost :as cb)
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(import numpy :as np)
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(import pandas :as pd)
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(import rdkit [Chem RDLogger])
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(import tqdm [tqdm])
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(import maplight-gnn)
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(defclass DrugBank []
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(setv namespaces {"" "http://www.drugbank.ca"})
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(defmacro ap-find [element name if-found]
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`(do
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(setv it (.find ~element ~name self.namespaces))
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(if-let it ~if-found)))
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(defmacro if-let [maybe execute]
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`(when (is-not ~maybe None)
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~execute))
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(meth __init__ [@filename @ids @id-types names]
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(setv @names (.str.lower names))
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(setv @get-ids {"ChEBI" @chebi
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"ChEMBL" @chembl
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"drugbank-id" @drugbank
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"InChIKey" @inchikey
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"PubChem Compound" @pubchem-compound
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"PubChem Substance" @pubchem-substance
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"unii" @unii}))
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(meth get-matches []
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(for [#(_ element) (tqdm (ET.iterparse @filename ["end"]))]
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;; don't care about non-drug entries
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(when (!= (cut element.tag 24 None) "drug")
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(continue))
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(setv matches (@check-match element))
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;; make sure there are matches before doing more work
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(when (not (matches.any))
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(continue))
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(yield #(matches element))))
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(meth check-match [element]
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(setv matches (pd.Series False :index @ids.index))
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(for [#(id-type id-func) (.items @get-ids)]
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(setv id-val (id-func element))
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(when (is id-val None) (continue))
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(setv id-matches (& (= @id-types id-type) (= @ids id-val)))
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(setv matches (| matches id-matches)))
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;; names can't use the same logic as the other id types
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(setv #(generic-names brand-names) (@all-names element))
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(setv matches (| matches (@names.isin generic-names)))
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(setv matches (| matches (@names.isin brand-names)))
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(return matches))
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(meth all-names [element]
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(setv generic-names (set))
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(setv brand-names (set))
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(setv main-name (@name element))
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(when (is-not main-name None) (generic-names.add (.lower main-name)))
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(ap-find element "synonyms"
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(for [synonym (.iter it)]
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(when (and (is-not synonym None) (is-not synonym.text None))
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(generic-names.add (.lower synonym.text)))))
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(ap-find element "products"
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(for [product (.iter it)]
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(setv brand-name (product.find "name" @namespaces))
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(if-let brand-name (brand-names.add (.lower brand-name.text)))))
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(setv generic-names (tuple (filter (fn [s] (not-in "\n" s)) generic-names)))
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(setv brand-names (tuple (filter (fn [s] (not-in "\n" s)) brand-names)))
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(return #(generic-names brand-names)))
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(meth cas-number [element]
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(ap-find element "cas-number" it.text))
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(meth chebi [element]
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(@from-external-identifiers element "ChEBI"))
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(meth chembl [element]
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(@from-external-identifiers element "ChEMBL"))
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(meth drugbank [element]
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(ap-find element "drugbank-id" it.text))
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(meth fda-approval [element]
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(ap-find element "groups" (in "approved" (tuple (it.itertext)))))
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(meth inchikey [element]
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(@from-calculated-properties element "InChIKey"))
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(meth indication [element]
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(ap-find element "indication" it.text))
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(meth mechanism [element]
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(ap-find element "mechanism-of-action" it.text))
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(meth name [element]
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(ap-find element "name" it.text))
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(meth prices [element]
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(ap-find element "prices"
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(do
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(setv prices (list))
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(for [price-element (it.iterfind "price" @namespaces)]
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(setv price (price-element.find "cost" @namespaces))
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(if-let price (.append prices (+ price.text (price.attrib.get "currency")))))
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(return prices))))
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(meth pubchem-compound [element]
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(@from-external-identifiers element "PubChem Compound"))
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(meth pubchem-substance [element]
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(@from-external-identifiers element "PubChem Substance"))
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(meth smiles [element]
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(@from-calculated-properties element "SMILES"))
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(meth unii [element]
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(ap-find element "unii" it.text))
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(meth from-external-identifiers [element resource-type]
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(ap-find element "external-identifiers"
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(for [external-identifier (it.iterfind "external-identifier" @namespaces)]
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(when (= (external-identifier.findtext "resource" :namespaces @namespaces) resource-type)
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(return (external-identifier.findtext "identifier" :namespaces @namespaces))))))
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(meth from-calculated-properties [element kind-type]
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(ap-find element "calculated-properties"
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(for [property (it.iterfind "property" @namespaces)]
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(when (= (property.findtext "kind" :namespaces @namespaces) kind-type)
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(return (property.findtext "value" :namespaces @namespaces)))))))
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(defclass DataAugmenter []
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(defmacro create-var-column [var-name col-name col-initial-value]
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`(do
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(setv ~var-name ~col-name)
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(setv (get self.drug-list ~var-name) ~col-initial-value)))
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(meth __init__ [@filename]
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(setv @drug-list None)
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(setv @admet-models None))
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(meth load-drug-queries []
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(cond
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(@filename.endswith ".csv")
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(with [f (open @filename "r")]
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(setv @drug-list (pd.read-csv f)))
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(@filename.endswith ".json")
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(with [f (open @filename "r")]
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(setv @drug-list (pd.read-json f :orient "records")))
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True
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(raise (ValueError "Data file must be .csv or .json")))
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(return self))
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(meth load-admet-models [models]
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(setv @admet-models (dict))
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(for [#(name path) (models.items)]
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(setv model (cb.CatBoostClassifier))
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(model.load-model path)
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(setv (get @admet-models name) model))
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(return self))
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(meth save-drug-info [filename]
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(when (is @drug-list None)
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(raise (ValueError "drug-list must be loaded first.")))
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(with [f (open filename "w")]
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(@drug-list.to-json f :orient "records")))
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(meth match-drugbank [filename id-col-name id-type-col-name name-col-name]
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(when (is @drug-list None)
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(raise (ValueError "drug-list is not defined. Call load-drug-queries before match-drugbank.")))
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;; make sure the cols are strings and not lists of strings
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(setv unwrap-list (fn [x] (if (isinstance x list) (get x 0) x)))
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(setv id-col (.apply (get @drug-list id-col-name) unwrap-list))
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(setv id-type-col (.apply (get @drug-list id-type-col-name) unwrap-list))
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(setv name-col (.apply (get @drug-list name-col-name) unwrap-list))
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;; tedious column making for what we're about to store
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;; variable name, column title, initial value
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(create-var-column cas-column "CAS Registry Number" None)
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(create-var-column fda-column "FDA Approved" None)
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(create-var-column indication-column "Indication" None)
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(create-var-column mechanism-column "Mechanism" None)
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(create-var-column name-column "DrugBank Name" None)
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(create-var-column price-column "Prices" (@drug-list.apply (fn [_] (list)) :axis 1))
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(create-var-column smiles-column "SMILES" None)
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(create-var-column unii-column "UNII" None)
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(setv drugbank (DrugBank filename id-col id-type-col name-col))
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(for [#(matches element) (drugbank.get-matches)]
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(setv (ncut @drug-list.loc matches cas-column) (drugbank.cas-number element))
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(setv (ncut @drug-list.loc matches fda-column) (drugbank.fda-approval element))
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(setv (ncut @drug-list.loc matches indication-column) (drugbank.indication element))
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(setv (ncut @drug-list.loc matches mechanism-column) (drugbank.mechanism element))
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(setv (ncut @drug-list.loc matches name-column) (drugbank.name element))
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(setv (ncut @drug-list.loc matches price-column)
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(.apply (ncut @drug-list.loc matches price-column) (fn [_] (drugbank.prices element)))) ; prices is a list
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(setv (ncut @drug-list.loc matches smiles-column) (drugbank.smiles element))
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(setv (ncut @drug-list.loc matches unii-column) (drugbank.unii element))))
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(meth deduplicate []
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(when (is @drug-list None)
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(raise (ValueError "drug-list is not defined. Call load-drug-queries before deduplicate.")))
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(when (not-in "DrugBank Name" @drug-list.columns)
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(raise (ValueError "ID data does not exist yet. Run match-drugbank to create it.")))
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(setv @drug-list
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(-> @drug-list
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(.groupby "DrugBank Name")
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(.agg
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(fn [x]
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(setv y [])
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(for [item x]
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(if (isinstance item list)
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(y.extend item)
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(y.append item)))
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(setv z (set y))
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(z.discard None)
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(cond
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(= (len z) 0) None
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(= (len z) 1) (.pop z)
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True z)))
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(.reset-index))))
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(meth predict-admet []
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(when (is @drug-list None)
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(raise (ValueError "drug-list is not defined. Call load-drug-queries before predict-admet.")))
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(when (is @admet-models None)
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(raise (ValueError "admet-models is not defined. Call load-admet-models before predict-admet.")))
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(when (not-in "SMILES" @drug-list.columns)
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(raise (ValueError "SMILES data does not exist yet. Run match-drugbank to create it.")))
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(RDLogger.DisableLog "rdApp.*")
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(setv smiles-mask (.notna (get @drug-list "SMILES")))
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(setv smiles (ncut @drug-list.loc smiles-mask "SMILES"))
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(setv molecules (smiles.apply Chem.MolFromSmiles))
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(setv molecules-mask (.notna molecules))
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(setv fingerprints (@get-fingerprints (get molecules molecules-mask)))
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(setv combined-mask (pd.Series False :index @drug-list.index))
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(setv (ncut combined-mask.loc (. (get smiles molecules-mask) index)) True)
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(for [#(name model) (@admet-models.items)]
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(setv predictions (model.predict-proba fingerprints))
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(setv (ncut @drug-list.loc combined-mask name) (ncut predictions : 1))))
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(meth get-fingerprints [molecules]
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(setv fingerprints (list))
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(fingerprints.append (maplight-gnn.get-morgan-fingerprints molecules))
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(fingerprints.append (maplight-gnn.get-avalon-fingerprints molecules))
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(fingerprints.append (maplight-gnn.get-erg-fingerprints molecules))
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(fingerprints.append (maplight-gnn.get-rdkit-features molecules))
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(fingerprints.append (maplight-gnn.get-gin-supervised-masking molecules))
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(np.concatenate fingerprints :axis 1)))
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(when (= __name__ "__main__")
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(setv augmenter
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(-> (DataAugmenter "data/translator_drugs.json")
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(.load-drug-queries)
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(.load-admet-models {"Blood Brain Barrier" "data/admet/bbb_martins-0.916-0.002.dump" "Bioavailability" "data/admet/bioavailability_ma-0.74-0.01.dump" "Human Intestinal Absorption" "data/admet/hia_hou-0.989-0.001.dump"})))
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(doto augmenter
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(.match-drugbank "data/src/drugbank.xml" "result_id" "id_type" "result_name")
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(.deduplicate)
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(.predict-admet)
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(.save-drug-info "data/translator_drug_list.json")))
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347
test/highlight/data_augmenter.py
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347
test/highlight/data_augmenter.py
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@ -0,0 +1,347 @@
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import hy
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import xml.etree.ElementTree as ET
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hy.macros.require('hyrule', None, target_module_name='data_augmenter', assignments=[['->', '->'], ['doto', 'doto'], ['meth', 'meth'], ['ncut', 'ncut']], prefix='')
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import catboost as cb
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import numpy as np
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import pandas as pd
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from rdkit import Chem, RDLogger
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from tqdm import tqdm
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import maplight_gnn
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class DrugBank:
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namespaces = {'': 'http://www.drugbank.ca'}
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_hy_local_macro__ap_find = lambda element, name, if_found: hy.models.Expression([hy.models.Symbol('do', from_parser=True), hy.models.Expression([hy.models.Symbol('setv', from_parser=True), hy.models.Symbol('it', from_parser=True), hy.models.Expression([hy.models.Expression([hy.models.Symbol('.', from_parser=True), hy.models.Symbol('None', from_parser=True), hy.models.Symbol('find', from_parser=True)]), element, name, hy.models.Expression([hy.models.Symbol('.', from_parser=True), hy.models.Symbol('self', from_parser=True), hy.models.Symbol('namespaces', from_parser=True)])])]), hy.models.Expression([hy.models.Symbol('if-let', from_parser=True), hy.models.Symbol('it', from_parser=True), if_found])])
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_hy_local_macro__if_let = lambda maybe, execute: hy.models.Expression([hy.models.Symbol('when', from_parser=True), hy.models.Expression([hy.models.Symbol('is-not', from_parser=True), maybe, hy.models.Symbol('None', from_parser=True)]), execute])
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def __init__(self, filename, ids, id_types, names):
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None
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self.filename = filename
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self.ids = ids
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self.id_types = id_types
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self.names = names.str.lower()
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self.get_ids = {'ChEBI': self.chebi, 'ChEMBL': self.chembl, 'drugbank-id': self.drugbank, 'InChIKey': self.inchikey, 'PubChem Compound': self.pubchem_compound, 'PubChem Substance': self.pubchem_substance, 'unii': self.unii}
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def get_matches(self):
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None
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for (_, element) in tqdm(ET.iterparse(self.filename, ['end'])):
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if element.tag[24:None:None] != 'drug':
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continue
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_hy_anon_var_1 = None
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else:
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_hy_anon_var_1 = None
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matches = self.check_match(element)
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if not matches.any():
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continue
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_hy_anon_var_2 = None
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else:
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_hy_anon_var_2 = None
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yield (matches, element)
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def check_match(self, element):
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None
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matches = pd.Series(False, index=self.ids.index)
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for (id_type, id_func) in self.get_ids.items():
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id_val = id_func(element)
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if id_val is None:
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continue
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_hy_anon_var_3 = None
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else:
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_hy_anon_var_3 = None
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id_matches = (self.id_types == id_type) & (self.ids == id_val)
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matches = matches | id_matches
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(generic_names, brand_names) = self.all_names(element)
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matches = matches | self.names.isin(generic_names)
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matches = matches | self.names.isin(brand_names)
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return matches
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def all_names(self, element):
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None
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generic_names = set()
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brand_names = set()
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main_name = self.name(element)
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generic_names.add(main_name.lower()) if main_name is not None else None
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it = element.find('synonyms', self.namespaces)
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if it is not None:
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for synonym in it.iter():
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generic_names.add(synonym.text.lower()) if synonym is not None and synonym.text is not None else None
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_hy_anon_var_4 = None
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else:
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_hy_anon_var_4 = None
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it = element.find('products', self.namespaces)
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if it is not None:
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for product in it.iter():
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brand_name = product.find('name', self.namespaces)
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brand_names.add(brand_name.text.lower()) if brand_name is not None else None
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_hy_anon_var_5 = None
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else:
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_hy_anon_var_5 = None
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generic_names = tuple(filter(lambda s: '\n' not in s, generic_names))
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brand_names = tuple(filter(lambda s: '\n' not in s, brand_names))
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return (generic_names, brand_names)
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def cas_number(self, element):
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None
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it = element.find('cas-number', self.namespaces)
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return it.text if it is not None else None
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def chebi(self, element):
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None
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return self.from_external_identifiers(element, 'ChEBI')
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def chembl(self, element):
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None
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return self.from_external_identifiers(element, 'ChEMBL')
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def drugbank(self, element):
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None
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it = element.find('drugbank-id', self.namespaces)
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return it.text if it is not None else None
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def fda_approval(self, element):
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None
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it = element.find('groups', self.namespaces)
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return 'approved' in tuple(it.itertext()) if it is not None else None
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def inchikey(self, element):
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None
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return self.from_calculated_properties(element, 'InChIKey')
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def indication(self, element):
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None
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it = element.find('indication', self.namespaces)
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return it.text if it is not None else None
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def mechanism(self, element):
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None
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it = element.find('mechanism-of-action', self.namespaces)
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return it.text if it is not None else None
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def name(self, element):
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None
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it = element.find('name', self.namespaces)
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return it.text if it is not None else None
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def prices(self, element):
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None
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it = element.find('prices', self.namespaces)
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if it is not None:
|
||||
prices = list()
|
||||
for price_element in it.iterfind('price', self.namespaces):
|
||||
price = price_element.find('cost', self.namespaces)
|
||||
prices.append(price.text + price.attrib.get('currency')) if price is not None else None
|
||||
return prices
|
||||
_hy_anon_var_6 = None
|
||||
else:
|
||||
_hy_anon_var_6 = None
|
||||
return _hy_anon_var_6
|
||||
|
||||
def pubchem_compound(self, element):
|
||||
None
|
||||
return self.from_external_identifiers(element, 'PubChem Compound')
|
||||
|
||||
def pubchem_substance(self, element):
|
||||
None
|
||||
return self.from_external_identifiers(element, 'PubChem Substance')
|
||||
|
||||
def smiles(self, element):
|
||||
None
|
||||
return self.from_calculated_properties(element, 'SMILES')
|
||||
|
||||
def unii(self, element):
|
||||
None
|
||||
it = element.find('unii', self.namespaces)
|
||||
return it.text if it is not None else None
|
||||
|
||||
def from_external_identifiers(self, element, resource_type):
|
||||
None
|
||||
it = element.find('external-identifiers', self.namespaces)
|
||||
if it is not None:
|
||||
for external_identifier in it.iterfind('external-identifier', self.namespaces):
|
||||
if external_identifier.findtext('resource', namespaces=self.namespaces) == resource_type:
|
||||
return external_identifier.findtext('identifier', namespaces=self.namespaces)
|
||||
_hy_anon_var_7 = None
|
||||
else:
|
||||
_hy_anon_var_7 = None
|
||||
_hy_anon_var_8 = None
|
||||
else:
|
||||
_hy_anon_var_8 = None
|
||||
return _hy_anon_var_8
|
||||
|
||||
def from_calculated_properties(self, element, kind_type):
|
||||
None
|
||||
it = element.find('calculated-properties', self.namespaces)
|
||||
if it is not None:
|
||||
for property in it.iterfind('property', self.namespaces):
|
||||
if property.findtext('kind', namespaces=self.namespaces) == kind_type:
|
||||
return property.findtext('value', namespaces=self.namespaces)
|
||||
_hy_anon_var_9 = None
|
||||
else:
|
||||
_hy_anon_var_9 = None
|
||||
_hy_anon_var_10 = None
|
||||
else:
|
||||
_hy_anon_var_10 = None
|
||||
return _hy_anon_var_10
|
||||
|
||||
class DataAugmenter:
|
||||
_hy_local_macro__create_var_column = lambda var_name, col_name, col_initial_value: hy.models.Expression([hy.models.Symbol('do', from_parser=True), hy.models.Expression([hy.models.Symbol('setv', from_parser=True), var_name, col_name]), hy.models.Expression([hy.models.Symbol('setv', from_parser=True), hy.models.Expression([hy.models.Symbol('get', from_parser=True), hy.models.Expression([hy.models.Symbol('.', from_parser=True), hy.models.Symbol('self', from_parser=True), hy.models.Symbol('drug-list', from_parser=True)]), var_name]), col_initial_value])])
|
||||
|
||||
def __init__(self, filename):
|
||||
None
|
||||
self.filename = filename
|
||||
self.drug_list = None
|
||||
self.admet_models = None
|
||||
|
||||
def load_drug_queries(self):
|
||||
None
|
||||
if self.filename.endswith('.csv'):
|
||||
_hy_anon_var_11 = None
|
||||
with open(self.filename, 'r') as f:
|
||||
self.drug_list = pd.read_csv(f)
|
||||
_hy_anon_var_11 = None
|
||||
_hy_anon_var_15 = _hy_anon_var_11
|
||||
else:
|
||||
if self.filename.endswith('.json'):
|
||||
_hy_anon_var_12 = None
|
||||
with open(self.filename, 'r') as f:
|
||||
self.drug_list = pd.read_json(f, orient='records')
|
||||
_hy_anon_var_12 = None
|
||||
_hy_anon_var_14 = _hy_anon_var_12
|
||||
else:
|
||||
if True:
|
||||
raise ValueError('Data file must be .csv or .json')
|
||||
_hy_anon_var_13 = None
|
||||
else:
|
||||
_hy_anon_var_13 = None
|
||||
_hy_anon_var_14 = _hy_anon_var_13
|
||||
_hy_anon_var_15 = _hy_anon_var_14
|
||||
return self
|
||||
|
||||
def load_admet_models(self, models):
|
||||
None
|
||||
self.admet_models = dict()
|
||||
for (name, path) in models.items():
|
||||
model = cb.CatBoostClassifier()
|
||||
model.load_model(path)
|
||||
self.admet_models[name] = model
|
||||
return self
|
||||
|
||||
def save_drug_info(self, filename):
|
||||
None
|
||||
if self.drug_list is None:
|
||||
raise ValueError('drug-list must be loaded first.')
|
||||
_hy_anon_var_16 = None
|
||||
else:
|
||||
_hy_anon_var_16 = None
|
||||
_hy_anon_var_17 = None
|
||||
with open(filename, 'w') as f:
|
||||
_hy_anon_var_17 = self.drug_list.to_json(f, orient='records')
|
||||
return _hy_anon_var_17
|
||||
|
||||
def match_drugbank(self, filename, id_col_name, id_type_col_name, name_col_name):
|
||||
None
|
||||
if self.drug_list is None:
|
||||
raise ValueError('drug-list is not defined. Call load-drug-queries before match-drugbank.')
|
||||
_hy_anon_var_18 = None
|
||||
else:
|
||||
_hy_anon_var_18 = None
|
||||
unwrap_list = lambda x: x[0] if isinstance(x, list) else x
|
||||
id_col = self.drug_list[id_col_name].apply(unwrap_list)
|
||||
id_type_col = self.drug_list[id_type_col_name].apply(unwrap_list)
|
||||
name_col = self.drug_list[name_col_name].apply(unwrap_list)
|
||||
cas_column = 'CAS Registry Number'
|
||||
self.drug_list[cas_column] = None
|
||||
fda_column = 'FDA Approved'
|
||||
self.drug_list[fda_column] = None
|
||||
indication_column = 'Indication'
|
||||
self.drug_list[indication_column] = None
|
||||
mechanism_column = 'Mechanism'
|
||||
self.drug_list[mechanism_column] = None
|
||||
name_column = 'DrugBank Name'
|
||||
self.drug_list[name_column] = None
|
||||
price_column = 'Prices'
|
||||
self.drug_list[price_column] = self.drug_list.apply(lambda _: list(), axis=1)
|
||||
smiles_column = 'SMILES'
|
||||
self.drug_list[smiles_column] = None
|
||||
unii_column = 'UNII'
|
||||
self.drug_list[unii_column] = None
|
||||
drugbank = DrugBank(filename, id_col, id_type_col, name_col)
|
||||
for (matches, element) in drugbank.get_matches():
|
||||
self.drug_list.loc[matches, cas_column] = drugbank.cas_number(element)
|
||||
self.drug_list.loc[matches, fda_column] = drugbank.fda_approval(element)
|
||||
self.drug_list.loc[matches, indication_column] = drugbank.indication(element)
|
||||
self.drug_list.loc[matches, mechanism_column] = drugbank.mechanism(element)
|
||||
self.drug_list.loc[matches, name_column] = drugbank.name(element)
|
||||
self.drug_list.loc[matches, price_column] = self.drug_list.loc[matches, price_column].apply(lambda _: drugbank.prices(element))
|
||||
self.drug_list.loc[matches, smiles_column] = drugbank.smiles(element)
|
||||
self.drug_list.loc[matches, unii_column] = drugbank.unii(element)
|
||||
|
||||
def deduplicate(self):
|
||||
None
|
||||
if self.drug_list is None:
|
||||
raise ValueError('drug-list is not defined. Call load-drug-queries before deduplicate.')
|
||||
_hy_anon_var_19 = None
|
||||
else:
|
||||
_hy_anon_var_19 = None
|
||||
if 'DrugBank Name' not in self.drug_list.columns:
|
||||
raise ValueError('ID data does not exist yet. Run match-drugbank to create it.')
|
||||
_hy_anon_var_20 = None
|
||||
else:
|
||||
_hy_anon_var_20 = None
|
||||
|
||||
def _hy_anon_var_21(x):
|
||||
y = []
|
||||
for item in x:
|
||||
y.extend(item) if isinstance(item, list) else y.append(item)
|
||||
z = set(y)
|
||||
z.discard(None)
|
||||
return None if len(z) == 0 else z.pop() if len(z) == 1 else z if True else None
|
||||
self.drug_list = self.drug_list.groupby('DrugBank Name').agg(_hy_anon_var_21).reset_index()
|
||||
|
||||
def predict_admet(self):
|
||||
None
|
||||
if self.drug_list is None:
|
||||
raise ValueError('drug-list is not defined. Call load-drug-queries before predict-admet.')
|
||||
_hy_anon_var_22 = None
|
||||
else:
|
||||
_hy_anon_var_22 = None
|
||||
if self.admet_models is None:
|
||||
raise ValueError('admet-models is not defined. Call load-admet-models before predict-admet.')
|
||||
_hy_anon_var_23 = None
|
||||
else:
|
||||
_hy_anon_var_23 = None
|
||||
if 'SMILES' not in self.drug_list.columns:
|
||||
raise ValueError('SMILES data does not exist yet. Run match-drugbank to create it.')
|
||||
_hy_anon_var_24 = None
|
||||
else:
|
||||
_hy_anon_var_24 = None
|
||||
RDLogger.DisableLog('rdApp.*')
|
||||
smiles_mask = self.drug_list['SMILES'].notna()
|
||||
smiles = self.drug_list.loc[smiles_mask, 'SMILES']
|
||||
molecules = smiles.apply(Chem.MolFromSmiles)
|
||||
molecules_mask = molecules.notna()
|
||||
fingerprints = self.get_fingerprints(molecules[molecules_mask])
|
||||
combined_mask = pd.Series(False, index=self.drug_list.index)
|
||||
combined_mask.loc[smiles[molecules_mask].index] = True
|
||||
for (name, model) in self.admet_models.items():
|
||||
predictions = model.predict_proba(fingerprints)
|
||||
self.drug_list.loc[combined_mask, name] = predictions[slice(None, None), 1]
|
||||
|
||||
def get_fingerprints(self, molecules):
|
||||
None
|
||||
fingerprints = list()
|
||||
fingerprints.append(maplight_gnn.get_morgan_fingerprints(molecules))
|
||||
fingerprints.append(maplight_gnn.get_avalon_fingerprints(molecules))
|
||||
fingerprints.append(maplight_gnn.get_erg_fingerprints(molecules))
|
||||
fingerprints.append(maplight_gnn.get_rdkit_features(molecules))
|
||||
fingerprints.append(maplight_gnn.get_gin_supervised_masking(molecules))
|
||||
return np.concatenate(fingerprints, axis=1)
|
||||
if __name__ == '__main__':
|
||||
augmenter = DataAugmenter('data/translator_drugs.json').load_drug_queries().load_admet_models({'Blood Brain Barrier': 'data/admet/bbb_martins-0.916-0.002.dump', 'Bioavailability': 'data/admet/bioavailability_ma-0.74-0.01.dump', 'Human Intestinal Absorption': 'data/admet/hia_hou-0.989-0.001.dump'})
|
||||
_hy_gensym_f_1 = augmenter
|
||||
_hy_gensym_f_1.match_drugbank('data/src/drugbank.xml', 'result_id', 'id_type', 'result_name')
|
||||
_hy_gensym_f_1.deduplicate()
|
||||
_hy_gensym_f_1.predict_admet()
|
||||
_hy_gensym_f_1.save_drug_info('data/translator_drug_list.json')
|
||||
_hy_anon_var_25 = _hy_gensym_f_1
|
||||
else:
|
||||
_hy_anon_var_25 = None
|
||||
30
test/highlight/highlight_examples.hy
Normal file
30
test/highlight/highlight_examples.hy
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
(setv foobar (+ 2 2))
|
||||
(setv [tim eric] ["jim" "derrick"])
|
||||
(setv alpha "a" beta "b")
|
||||
|
||||
(sorted "abcBC"
|
||||
:key (fn [x] (.lower x)))
|
||||
|
||||
(defn test [a b [c "x"] #* d]
|
||||
[a b c d])
|
||||
|
||||
(with [o (open "file.txt" "rt")]
|
||||
(setv buffer [])
|
||||
(while (< (len buffer) 10)
|
||||
(.append buffer (next o))))
|
||||
|
||||
(lfor
|
||||
x (range 3)
|
||||
y (range 3)
|
||||
:if (= (+ x y) 3)
|
||||
(* x y))
|
||||
|
||||
(defmacro do-while [test #* body]
|
||||
`(do
|
||||
~@body
|
||||
(while ~test
|
||||
~@body)))
|
||||
|
||||
(setv x 0)
|
||||
(do-while x
|
||||
(print "Printed once."))
|
||||
Loading…
Add table
Add a link
Reference in a new issue