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  • John Snow Labs
  • Nov 21, 2022

Mapping Drugs from the KEGG Database to Their Efficacies, Molecular Weights and Corresponding Codes from Other Databases

  • drug
  • efficacy
  • molecular_weight
  • cas
  • pubchem
  • chebi
  • ligandbox
  • nikkaji
  • pdbcct
  • chunk_mapper
  • clinical
  • en
  • licensed

Description

This pretrained model maps drugs with their corresponding efficacy, molecular_weight as well as CAS, PubChem, ChEBI, LigandBox, NIKKAJI, PDB-CCD codes. This model was trained with the data from the KEGG database.

Predicted Entities

efficacy, molecular_weight, CAS, PubChem, ChEBI, LigandBox, NIKKAJI, PDB-CCD

Open in Colab Copy S3 URI

How to use

document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentence_detector = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols("sentence")\
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

ner = MedicalNerModel.pretrained("ner_posology", "en", "clinical/models") \
    .setInputCols(["sentence", "token", "embeddings"]) \
    .setOutputCol("ner")

converter = NerConverter() \
    .setInputCols(["sentence", "token", "ner"]) \
    .setOutputCol("ner_chunk")\

chunkerMapper = ChunkMapperModel.pretrained("kegg_drug_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["efficacy", "molecular_weight", "CAS", "PubChem", "ChEBI", "LigandBox", "NIKKAJI", "PDB-CCD"])\

pipeline = Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer, 
    word_embeddings,
    ner, 
    converter, 
    chunkerMapper])


text= "She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin"

data = spark.createDataFrame([[text]]).toDF("text")

result = pipeline.fit(data).transform(data)

val document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentence_detector = new SentenceDetector()
    .setInputCols(Array("document"))
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")

val ner = MedicalNerModel.pretrained("ner_posology", "en", "clinical/models") 
    .setInputCols(Array("sentence", "token", "embeddings")) 
    .setOutputCol("ner")

val converter = new NerConverter() 
    .setInputCols(Array("sentence", "token", "ner")) 
    .setOutputCol("ner_chunk")

val chunkerMapper = ChunkMapperModel.pretrained("kegg_drug_mapper", "en", "clinical/models")
    .setInputCols("ner_chunk")
    .setOutputCol("mappings")
    .setRels(Array("efficacy", "molecular_weight", "CAS", "PubChem", "ChEBI", "LigandBox", "NIKKAJI", "PDB-CCD"))


val pipeline = new Pipeline().setStages(Array(
    document_assembler,
    sentence_detector,
    tokenizer, 
    word_embeddings,
    ner, 
    converter, 
    chunkerMapper))


val text= "She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin"

val data = Seq(text).toDS.toDF("text")

val result= pipeline.fit(data).transform(data)

import nlu
nlu.load("en.map_entity.kegg_drug").predict("""She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin""")

Results

+-------------+--------------------------------------------------+----------------+----------+-----------+-------+---------+---------+-------+
|    ner_chunk|                                          efficacy|molecular_weight|       CAS|    PubChem|  ChEBI|LigandBox|  NIKKAJI|PDB-CCD|
+-------------+--------------------------------------------------+----------------+----------+-----------+-------+---------+---------+-------+
|    OxyContin|     Analgesic (narcotic), Opioid receptor agonist|        351.8246|  124-90-3|  7847912.0| 7859.0|   D00847|J281.239H|   NONE|
|   folic acid|Anti-anemic, Hematopoietic, Supplement (folic a...|        441.3975|   59-30-3|  7847138.0|27470.0|   D00070|  J1.392G|    FOL|
|levothyroxine|                     Replenisher (thyroid hormone)|          776.87|   51-48-9|9.6024815E7|18332.0|   D08125|  J4.118A|    T44|
|      Norvasc|Antihypertensive, Vasodilator, Calcium channel ...|        408.8759|88150-42-9|5.1091781E7| 2668.0|   D07450| J33.383B|   NONE|
|      aspirin|Analgesic, Anti-inflammatory, Antipyretic, Anti...|        180.1574|   50-78-2|  7847177.0|15365.0|   D00109|  J2.300K|    AIN|
|    Neurontin|                     Anticonvulsant, Antiepileptic|        171.2368|60142-96-3|  7847398.0|42797.0|   D00332| J39.388F|    GBN|
+-------------+--------------------------------------------------+----------------+----------+-----------+-------+---------+---------+-------+

Model Information

Model Name: kegg_drug_mapper
Compatibility: Healthcare NLP 4.2.2+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 1.0 MB